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Data Platform Engineering

Enterprise Data Engineering Services

We design, build, and operationalise modern data platforms - from lakehouse architecture and ELT pipelines to real-time streaming and governed data catalogues - that give your organisation a reliable, scalable foundation for analytics, AI, and operational intelligence.

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500+
Enterprise Clients
10,000+
Engineers Deployed
50+
Countries Served
99.4%
CSAT Score
48h
Team Assembly

The Challenge

Fragmented data infrastructure is silently eroding your competitive position

Most enterprises carry years of accumulated data debt: disconnected pipelines, undocumented datasets, inconsistent transformation logic, and no single source of truth. This fragmentation drives up analyst cycle times, undermines model reliability, and creates compliance exposure that only becomes visible during an audit.

73%
of analytics projects delayed by data quality issues
40+
hours per week lost to manual data reconciliation
$12M
average annual cost of poor data quality per enterprise
3x
longer time-to-insight without a governed data platform

Why QuickHire

Why Enterprises Choose QuickHire

01

Lakehouse-First Architecture

We design on open table formats (Delta Lake, Iceberg) that eliminate redundant data copies and unify BI and ML workloads on a single storage tier. Our architects select the right compute engine for each workload - Spark, Trino, or warehouse SQL - without forcing a single vendor dependency.

02

Production-Grade ELT Pipelines

Our dbt and Spark pipeline patterns are built for incremental loading, automated testing, and CI/CD promotion from development through production. Every transformation is version-controlled, documented, and covered by data quality contracts before it reaches downstream consumers.

03

Real-Time Streaming Expertise

We implement Kafka, Kinesis, and Flink architectures that deliver sub-second event processing with exactly-once semantics and schema governance. Our streaming designs are operationally mature, covering partition strategy, consumer group management, and failure recovery patterns.

04

Embedded Data Quality

Quality gates powered by Great Expectations are integrated at every pipeline stage, automatically blocking bad data and publishing quality reports to a self-service portal. This shifts quality assurance left and eliminates the manual validation work that consumes analyst time.

05

Governed Data Cataloguing

We implement DataHub or Apache Atlas catalogues with automated metadata ingestion, end-to-end lineage from source to dashboard, and a tagging taxonomy aligned to your classification policy. Data consumers find trusted, well-documented assets in minutes rather than days.

06

Regulatory-Ready Governance

Our governance frameworks embed column-level security, PII detection, retention policies, and audit logging directly into the platform layer rather than treating compliance as an afterthought. We have delivered GDPR, CCPA, and HIPAA-compliant data platforms across regulated industries.

Challenges

Common Enterprise Pain Points

01

Data Silos and Inconsistent Definitions

When every business unit maintains its own ETL scripts and metric definitions, the same KPI reports different numbers in Finance, Sales, and Product - eroding trust in data assets and triggering expensive cross-team reconciliation cycles. Establishing a unified transformation layer with agreed business logic is the only durable solution.

02

Pipeline Fragility and Operational Overhead

Legacy ETL pipelines built without observability, testing, or documentation break silently and require specialist knowledge to diagnose, creating a high operational burden on small data engineering teams. Modern orchestration with structured monitoring, retry logic, and runbook automation dramatically reduces mean time to recovery.

03

Scaling Costs Without Scaling Insight

Data warehousing costs frequently grow faster than the business value they generate when storage, compute, and egress are not governed, leading to budget pressure that forces organisations to limit data access rather than expand it. A well-architected lakehouse with tiered storage and query governance reverses this dynamic.

04

Compliance and Access Control Gaps

As data volumes grow, manually managing who can access what data becomes impractical, and auditors increasingly require demonstrable evidence of access control, lineage, and data handling procedures. Platform-level security automation - row-level security, attribute-based access control, automated deprovisioning - is essential at enterprise scale.

05

Machine Learning Teams Blocked on Data

ML and AI teams frequently spend 60 to 80 percent of their time on data preparation rather than model development, because there is no shared feature store, no reusable transformation library, and no point-in-time correct dataset generation capability. A modern data platform that exposes high-quality, versioned features accelerates every ML initiative downstream.

Our Approach

A unified data platform that is reliable, governed, and ready for AI

We deliver an end-to-end data engineering programme that establishes the architecture, pipelines, quality controls, and governance structures your organisation needs to treat data as a strategic asset. Our platform-first approach ensures that every capability we build - streaming ingestion, transformation, cataloguing, ML feature serving - operates as part of a cohesive, observable, and continuously improving system.

01
Lakehouse Architecture Design
Cloud-agnostic lakehouse blueprints on Delta Lake or Iceberg, with medallion zone design (bronze, silver, gold), compute engine selection, and migration planning from legacy warehouses.
02
ELT and Transformation Engineering
Production dbt projects with modular, tested SQL transformation layers, plus Spark and Flink jobs for compute-intensive workloads, all deployed via CI/CD with automated data quality gates.
03
Real-Time Streaming Platform
Kafka or Kinesis event backbone with Flink stream processing, schema registry, consumer group governance, and end-to-end monitoring from event emission to downstream consumption.
04
Data Governance and Cataloguing
DataHub or Atlas catalogue with automated lineage, PII classification, column-level security policies, and a regulatory compliance framework covering GDPR, CCPA, and HIPAA requirements.

Delivery Models

How We Deliver

Platform Foundation Build

A fixed-scope engagement that delivers a production-ready data platform including lakehouse architecture, core ingestion pipelines, dbt transformation layers, orchestration, quality gates, and catalogue in a defined timeframe.

Timeline
12-20 weeks
Team Size
4-8 engineers
Specialist Augmentation

Embed senior data engineers with specific expertise - Flink, dbt, Snowflake, Databricks, or data governance - directly into your existing team to accelerate delivery or fill critical skill gaps.

Timeline
Flexible
Team Size
1-4 engineers
Managed Data Platform Service

Our team operates, monitors, and continuously improves your data platform under an SLA-backed retainer, including incident response, pipeline maintenance, and quarterly architecture reviews.

Timeline
Ongoing
Team Size
2-6 engineers

Capabilities

Technical Capability Matrix

Storage and Lakehouse
Delta LakeApache IcebergApache HudiSnowflakeDatabricks LakehouseGoogle BigQueryAmazon RedshiftAzure Synapse
Transformation and Orchestration
dbt Coredbt CloudApache SparkApache AirflowPrefectDagsterTrinoApache Hive
Streaming and Ingestion
Apache KafkaAmazon KinesisApache FlinkGoogle Pub/SubAzure Event HubsConfluent PlatformAirbyteFivetran
Quality, Governance, and Cataloguing
Great ExpectationsDataHubApache AtlasAWS Glue Data CatalogGoogle DataplexCollibraAlationMonte Carlo

Engagement Models

How We Engage

Choose the model that fits your programme governance, budget cycle, and team structure.

01

Staff Augmentation

Engineers embed directly under your management.

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02

Dedicated Developers

Full-time team aligned to your product roadmap.

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03

Managed Teams

End-to-end delivery with SLA-backed outcomes.

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04

Engineering Pods

Autonomous cross-functional pods per domain.

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05

Offshore Dev Centre

Permanent engineering base in India. Full IP ownership.

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06

Build-Operate-Transfer

We build and run it. You take ownership on schedule.

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Our Process

From Discovery to Delivery

1

Discovery and Data Audit

Days 1-5

We inventory your existing data sources, pipelines, warehouses, and governance artefacts to establish a clear baseline and identify the highest-priority gaps and risks.

2

Architecture Design and Review

Week 2

Our architects produce a target-state platform design covering storage, compute, ingestion, transformation, streaming, quality, and governance, reviewed with your technical and business stakeholders.

3

Foundation Build and Pipeline Development

Weeks 3-10

We stand up the core infrastructure, implement ingestion connectors, develop dbt model layers, and deploy orchestration with monitoring, delivering a working analytics layer incrementally.

4

Quality Gates, Cataloguing, and Governance

Weeks 8-14

Great Expectations suites, DataHub automated ingestion, PII classification, access control policies, and audit logging are embedded and validated across all platform layers.

5

Operationalisation and Knowledge Transfer

Final 4 weeks

We deliver runbooks, architecture documentation, CI/CD pipelines, and team enablement sessions, transitioning the platform to your team with a hypercare support period.

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Security & Compliance

Enterprise-Grade Security by Default

✓ISO 27001 Certified✓SOC 2 Type II Ready✓GDPR Compliant✓DPDP Act Ready✓NDA on Day 1✓MSA Available✓IP Assignment Clauses✓Escrow Options

Governance

Programme Governance

✓

Data Classification Policy

Every dataset is tagged with a sensitivity tier (public, internal, confidential, restricted) in the catalogue, with technical access controls enforced automatically based on classification.

✓

Column-Level Security and PII Masking

Sensitive columns are protected by column-level security in the warehouse and masked or tokenised for non-privileged environments, with policies enforced at the query engine layer.

✓

Lineage and Audit Logging

End-to-end data lineage is captured from source system to downstream dashboard, and all data access events are streamed to a SIEM for anomaly detection and compliance reporting.

✓

Retention and Deletion Automation

Data retention schedules are configured as platform-level lifecycle policies, with automated deletion workflows that support GDPR right-to-erasure and CCPA deletion requests at scale.

Team Structure

Your Enterprise Team

Our data engineering engagements are staffed with senior engineers who hold deep specialisation across lakehouse architecture, streaming platforms, transformation frameworks, and data governance. We pair technical delivery with advisory support so your internal team inherits both a working platform and the knowledge to evolve it independently.

✓Data Platform Architect
✓Senior dbt Engineer
✓Apache Spark Engineer
✓Apache Kafka / Flink Engineer
✓Data Quality Engineer (Great Expectations)
✓Data Governance Specialist
✓DataHub / Catalogue Engineer
✓Data Platform DevOps / MLOps Engineer

Project Lifecycle

From Kickoff to Production

01
2 weeks

Discovery and Architecture

Data estate audit report, target-state architecture document, technology selection rationale, risk register, and phased delivery roadmap.

02
3-4 weeks

Infrastructure and Ingestion

Lakehouse environment provisioned, ingestion connectors deployed, raw data landing in bronze layer, orchestration DAGs active, and initial monitoring dashboards live.

03
4-6 weeks

Transformation and Quality

dbt project with silver and gold layers, Great Expectations suites covering all critical datasets, CI/CD pipeline with quality gates, and data freshness SLA monitoring.

04
3-4 weeks

Streaming and Advanced Capabilities

Kafka or Kinesis event backbone, Flink stream processing jobs, schema registry operational, and real-time datasets available in the lakehouse.

05
Ongoing

Governance, Cataloguing, and Handover

DataHub catalogue with automated lineage, PII classification, access control enforcement, compliance documentation, runbooks, and team enablement programme.

Case Studies

Enterprise Outcomes

Financial Services

A tier-one bank had 47 siloed ETL pipelines with no lineage documentation and a six-hour overnight batch window that regularly overran into trading hours.

We redesigned the platform on Databricks Lakehouse with dbt transformation layers and Airflow orchestration, reducing the batch window to under two hours and eliminating all lineage blind spots.

68%reduction in pipeline run time
Healthcare

A healthcare network needed to consolidate patient data from 12 source systems while maintaining HIPAA compliance and enabling a real-time readmission risk model.

We built a Snowflake lakehouse with column-level security, automated PII masking, and a Kafka streaming layer feeding a Feast feature store that served the risk model at sub-second latency.

$4.2Min avoided readmission penalties
Retail and E-Commerce

A global retailer could not reconcile inventory, sales, and logistics data across three warehouse systems, causing weekly analyst reconciliation cycles that consumed 200 hours of team capacity.

We implemented a dbt-based single source of truth with agreed metric definitions, automated quality gates, and a DataHub catalogue that gave every analyst access to the same governed datasets.

4xfaster time-to-insight for commercial teams

Start Your Engagement

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Hiring Models

One platform, two ways to hire

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Both models use the same vetted talent network · PM always included · Multi-country billing

Frequently Asked Questions

Enterprise data engineering covers the full lifecycle of designing, building, and operating data infrastructure that powers analytics, machine learning, and operational intelligence. This includes ingestion pipelines, storage layers (data lakes, warehouses, lakehouses), transformation frameworks, real-time streaming architectures, data quality systems, and metadata catalogues. A well-architected data platform ensures that data consumers - analysts, data scientists, and applications - receive trusted, timely, and well-documented datasets. Our engagements deliver all of these layers as a cohesive, governed platform rather than isolated point solutions.
A data lakehouse combines the low-cost, flexible storage of a data lake with the ACID transactional guarantees and SQL query performance of a data warehouse, typically implemented on open table formats such as Delta Lake, Apache Iceberg, or Apache Hudi. Enterprises should consider a lakehouse when they need to serve both structured BI workloads and unstructured ML workloads from a single storage tier, reducing data duplication and governance complexity. It is particularly valuable when teams struggle with stale warehouse copies of lake data or when storage and compute costs have grown unsustainably in a traditional two-tier architecture. Our architects assess your current state and design a migration path that minimises disruption to existing reporting workflows while unlocking the unified platform benefits.
We design ELT pipelines by separating concerns clearly: ingestion frameworks (Fivetran, Airbyte, custom Spark jobs) land raw data into a bronze layer, dbt models handle SQL-based transformation and business logic in silver and gold layers, and Spark is reserved for compute-intensive transformations that exceed SQL ergonomics - such as complex sessionisation, graph traversal, or large-scale ML feature engineering. dbt enables version-controlled, tested, and documented SQL transformations that data analysts can own without deep engineering support, while Spark provides the horsepower for petabyte-scale batch processing. Our pipeline standards include incremental materialisation strategies, data freshness SLAs, automated lineage capture, and CI/CD-gated promotion between environments.
We implement event-driven streaming architectures on Apache Kafka, Amazon Kinesis, Google Pub/Sub, and Azure Event Hubs, depending on your cloud footprint and throughput requirements. Stream processing is handled by Apache Flink for complex event processing and stateful computations, or by Spark Structured Streaming for teams with existing Spark expertise. Our designs address partitioning strategy, consumer group management, schema registry integration (Confluent Schema Registry or AWS Glue Schema Registry), and exactly-once delivery semantics to ensure pipeline correctness under failure. We also architect lambda and kappa patterns for organisations that need both real-time serving and historical batch reprocessing from the same data platform.
We embed Great Expectations (GX) as a first-class citizen in pipeline CI/CD, defining expectation suites that validate schemas, statistical distributions, referential integrity, and business rules at every transformation stage. Expectation suites are stored in version control alongside dbt models, so quality contracts evolve with the data model and failures block downstream promotion rather than silently propagating bad data. Data docs generated by GX are published to an internal portal, giving data consumers a continuously updated quality report for every dataset. For large-scale deployments we integrate GX with Airflow or Prefect orchestration, enabling quality gates to trigger automated quarantine, alerting, and incident tickets rather than requiring manual intervention.
Data cataloguing is the practice of creating a searchable, governed inventory of all data assets in the enterprise - tables, columns, dashboards, ML models, and their relationships - enriched with business context, ownership, classification tags, and lineage. We implement catalogues on DataHub, Apache Atlas, or cloud-native offerings such as AWS Glue Data Catalog and Google Dataplex, selecting the platform that best integrates with your existing ingestion and transformation stack. Effective cataloguing dramatically reduces the time analysts spend discovering and validating data, and provides the metadata foundation required for regulatory compliance and access governance. Our cataloguing projects include automated metadata ingestion from source systems, lineage extraction from dbt and Spark, and a tagging taxonomy aligned to your data classification policy.
Data governance at the enterprise level requires a combination of organisational policy, technical controls, and automated enforcement rather than relying solely on manual processes. We help clients establish a data governance framework that defines data ownership, classification tiers (public, internal, confidential, restricted), retention and deletion schedules, and access control models - then implement those policies through column-level security in warehouses, attribute-based access control in lake storage, and automated PII detection pipelines. Governance metadata is linked to the data catalogue so that every dataset carries its policy context, enabling auditors to query compliance posture programmatically. We also support GDPR, CCPA, and HIPAA compliance implementations, including right-to-erasure workflows and data lineage documentation for regulatory reporting.
Our engineers are proficient across the major cloud data platforms - Snowflake, Databricks, Google BigQuery, Amazon Redshift, Azure Synapse Analytics, and their surrounding ecosystem services. In multi-cloud scenarios we prioritise open table formats (Iceberg, Delta Lake) and portable compute frameworks (Spark, Flink, dbt) to avoid hard lock-in, while using cloud-native services where they deliver a significant operational advantage. We architect data mesh and data fabric patterns that allow domain teams to publish and consume data across cloud boundaries using standardised contracts and a shared catalogue. Our multi-cloud engagements also address network topology, cross-cloud latency, egress cost optimisation, and unified identity and access management.
We implement orchestration on Apache Airflow (managed via Astronomer or MWAA), Prefect, or Dagster, selecting the framework that best fits your team maturity and operational complexity. All pipelines are instrumented with structured logging, task-level metrics (row counts, processing duration, error rates), and alerting via PagerDuty or Opsgenie, so on-call engineers receive actionable alerts with sufficient context to diagnose failures without log diving. We implement SLA monitoring dashboards that surface pipeline health against agreed freshness targets, enabling data operations teams to proactively communicate delays to downstream consumers. Runbooks, retry policies, and circuit-breaker patterns are standardised across all DAGs to reduce mean time to recovery.
We offer three primary engagement models: a fixed-scope platform build for greenfield or migration projects with defined deliverables and timelines; a staff augmentation model for enterprises with existing teams that need specialised skills such as Flink or dbt; and a managed data engineering service where our team operates and evolves your data platform under an SLA-backed retainer. Hybrid models are also common - for example, our engineers lead a 12-week platform build, then transition operations to an augmented internal team with a 90-day hypercare period. All engagements begin with a discovery and architecture review phase to ensure the solution design is grounded in your actual data volumes, latency requirements, and organisational constraints.
Data mesh decentralises data ownership to domain teams - Finance, Marketing, Product, Operations - each responsible for producing and publishing data products that meet platform-wide quality and discoverability standards. Our data mesh implementations establish a central platform team responsible for self-serve infrastructure (the data plane), a federated governance model (shared policies enforced via automation), and a data catalogue that aggregates domain product metadata into a single discovery surface. We help organisations define the domain boundaries, data product contracts, and SLA tiers that make mesh ownership practical rather than creating ungoverned sprawl. Implementation is typically phased, starting with two or three high-value domains to demonstrate the model before scaling organisation-wide.
Security is embedded at every layer: network isolation (VPC peering, private endpoints), encryption at rest and in transit (TLS 1.3, cloud KMS-managed keys), row-level and column-level security in warehouse and lakehouse layers, and attribute-based access control in object storage. We integrate the data platform with enterprise identity providers (Okta, Azure AD) via SAML or OIDC, ensuring that data access policies are maintained centrally and that leavers are deprovisioned automatically. Sensitive data is identified through automated PII scanning, tagged in the catalogue, and subject to masking or tokenisation policies before reaching non-privileged query environments. Access audit logs are streamed to a SIEM for anomaly detection and compliance reporting.
Data platform costs grow rapidly when storage, compute, and egress are not actively managed, and we treat cost engineering as a first-class concern alongside performance and reliability. Our cost optimisation practices include right-sizing warehouse clusters with auto-suspend and auto-scale policies, implementing tiered storage (hot, warm, cold) with lifecycle policies that move infrequently accessed data to cheaper object storage tiers, and eliminating redundant data copies through lakehouse consolidation. Query cost governance - such as Snowflake resource monitors or BigQuery slot reservations - prevents runaway ad-hoc queries from inflating bills, while FinOps dashboards give engineering and finance teams shared visibility into spend by team, pipeline, and dataset. Clients typically achieve 30 to 50 percent cost reductions within six months of engaging our optimisation practice.
Modern data platforms serve as the foundation for ML feature stores, training data pipelines, and model monitoring infrastructure. We architect feature engineering pipelines that produce point-in-time correct feature sets for model training, avoiding training-serving skew by using the same transformation logic at both batch and online serving time. Feature stores (Feast, Tecton, or cloud-native options such as SageMaker Feature Store and Vertex AI Feature Store) are integrated with the lakehouse so that features are discoverable, versioned, and reusable across multiple models. We also implement data-centric ML monitoring that tracks input feature distribution drift, label drift, and data quality degradation as leading indicators of model performance decay, enabling proactive retraining rather than reactive incident response.
A mature data engineering function typically comprises a central platform engineering team responsible for the data infrastructure, orchestration, and governance tooling, and embedded domain data engineers who own pipelines and data products within business units. The platform team maintains the data highway - ingestion connectors, compute environments, quality frameworks, cataloguing automation, and CI/CD tooling - while domain engineers focus on business-specific transformation logic and data product SLAs. We recommend a data product owner role in each domain to translate business requirements into data contracts, and a data governance lead who bridges the technical catalogue and the organisational policy framework. Our advisory engagements help clients design this operating model, write role profiles, and build the capability development roadmap needed to staff it sustainably.
A greenfield data platform build - covering lakehouse architecture, core ingestion pipelines, dbt transformation layers, orchestration, quality gates, and catalogue - typically takes 12 to 20 weeks depending on the number of source systems, data volumes, and organisational complexity. Migration projects from legacy warehouses (Teradata, on-premises Hadoop, monolithic SQL Server ETL) carry additional complexity for schema translation, historical data backfill, and parallel-run validation, often extending timelines to 20 to 32 weeks for large estates. Our phased delivery model ensures that value is delivered incrementally - a working analytics layer is typically available within six to eight weeks - rather than requiring a big-bang cutover. We provide weekly milestone reporting, a shared delivery backlog, and executive steering checkpoints to keep stakeholders aligned throughout.
Industries
Financial ServicesHealthcare and Life SciencesRetail and E-CommerceTechnology and SaaSManufacturing and Supply Chain