Data Engineering
Building the data foundation that AI and analytics actually run on
Our Data Engineering Center of Excellence builds the foundational platforms that everything else — analytics, ML, AI, and operational reporting — depends on. We design and operate modern lakehouses, real-time streaming systems, data mesh implementations, and governance frameworks that make data trustworthy and accessible. Our engineers go deep on Databricks, Snowflake, Kafka, Flink, dbt, and Airflow, and they obsess about data quality, lineage, cost, and developer experience. Increasingly, our data work is shaped by AI: vector stores, feature platforms for ML, and the data foundations for GenAI and RAG.
Our 10-year commitment
AI is only as good as the data foundation underneath it. We are betting on a decade of investment in data platforms, governance, and AI-ready data architectures — making GreenPot the long-term partner for organizations whose analytics and AI ambitions depend on getting data right.
Services we provide
The full breadth of Data Engineering capability we deliver — from strategy and architecture through engineering and operations.
Modern Data Platform & Lakehouse Engineering
Greenfield and migration programs on Databricks, Snowflake, BigQuery, and open lakehouse stacks (Delta, Iceberg, Hudi).
Real-Time Streaming & Event Architectures
Kafka, Flink, Kinesis, and Pulsar-based streaming pipelines for fraud, personalization, IoT, and operational analytics.
ETL/ELT & Pipeline Engineering
Airflow, dbt, and Dagster pipelines designed for testability, observability, and cost discipline.
Data Governance, Quality & Lineage
Catalog implementations (Unity, Collibra, Alation), data-quality frameworks (Great Expectations, Soda), and end-to-end lineage.
Data Mesh & Self-Service Platforms
Domain-oriented data architectures, internal data product platforms, and self-service developer experiences.
AI-Ready Data Foundations
Feature stores, vector databases, RAG-grade indexing pipelines, and data contracts engineered for ML and GenAI workloads.
Migration & Modernization
Legacy warehouse and Hadoop migrations to cloud lakehouses — with parallel-run validation and zero-downtime cutovers.
Embedded Data Engineering Teams
Dedicated data-engineering pods outsourced into client platform teams to own data products and infrastructure over multi-year horizons.
Clients we have served
Our Data Engineering practice serves both product-led companies building the next generation of software and service-led firms reselling our capability to their end clients.
Client names anonymized to protect engagement confidentiality.
Product Companies
A US data-product unicorn
Data ProductsCo-build their managed data-pipeline product — engineers embedded in their platform and reliability orgs.
A global digital advertising product firm
AdtechArchitected and operate the real-time event pipeline processing tens of TB of telemetry daily inside their flagship product.
A North American observability product company
Observability / DevToolsBuilt the ingestion and storage pipeline for high-cardinality telemetry that powers their core product.
An EU mobility platform
Mobility / MarketplacesOwned the data foundation that supports their pricing, ETA, and supply-positioning ML systems.
Service Companies & SIs
A top global IT services firm
IT ServicesProvide a data-engineering bench staffing their banking, insurance, and retail modernization programs.
A Big-4 consulting major
Management ConsultingImplementation arm for several of their data-foundation and lakehouse migration engagements at Fortune 500 clients.
A US healthcare analytics consultancy
Healthcare AnalyticsJoint delivery of HIPAA-compliant data platforms for US payer and provider clients.
A specialist analytics partner (APAC)
Analytics ConsultingCapacity partner providing dbt, Airflow, and Snowflake engineering under their brand to regional enterprises.
Data engineers owning your platform alongside you
Data platforms aren't projects — they are products that live for a decade. Our model is to outsource senior data engineers into client platform teams where they own pipelines, lakehouse infrastructure, governance, and AI-ready data products as long-tenured members of those teams. That is how we power the data platforms behind several product unicorns and global SIs.
A US data-product unicorn
Embedded data-platform pod inside their R&D organization owning core ingestion and governance services.
A global IT services major
Data-engineering capacity center across their banking and insurance practice.
A North American observability product firm
Co-own the ingestion and storage pipeline behind a customer-visible product surface.
Selected Case Studies
Anonymized engagement stories. The full library lives in our case studies hub.
Lakehouse migration for a global bank
Problem
A global bank's legacy Hadoop estate had become too expensive, too slow, and a blocker to ML adoption across business units.
Approach
Designed a Databricks-based lakehouse, migrated hundreds of pipelines with parallel-run validation, implemented Unity Catalog governance, and stood up a self-service platform for downstream teams.
Outcome
Total cost of ownership dropped materially, pipeline run times collapsed, and ML teams across the bank could now stand up new use cases in days instead of months.
Impact
Real-time event platform for an adtech product
Problem
An adtech product company was hitting the limits of its batch-only data stack, blocking real-time bidding, attribution, and audience products.
Approach
Designed a Kafka + Flink streaming backbone, refactored downstream consumers to event-driven patterns, and embedded a data-platform pod that has owned the system since.
Outcome
The product line now bids in real time, attribution latency dropped from hours to seconds, and the streaming platform unlocked new product capabilities.
Impact
AI-ready data foundation for a GenAI program
Problem
An enterprise launching a GenAI program discovered its data was too messy, ungoverned, and disconnected for RAG and fine-tuning workflows.
Approach
Built a RAG-grade content pipeline with quality checks, lineage, access controls, and a vector indexing layer — embedded inside their existing lakehouse rather than as a side stack.
Outcome
GenAI program unblocked across multiple business units; data governance for AI use cases passed risk review on first attempt.
Impact
Technologies & Tools
The stack our Data Engineering engineers go deep on.
Partner with our Data Engineering CoE
Whether you need a dedicated pod, embedded engineers, or a full program — let's map your goals to our practice.
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