Azure · Databricks · Microsoft Fabric
20+ Years Senior-Led
Built-In QA

Data Engineering,
Built to Ship.

Modern lakehouse architecture. Production-grade pipelines. Senior engineers who’ve already solved the hard part.

Experience

20+ Years

Scale

Multi-TB

Cost Savings

30–50%

Launch Time

<9 Days

Traditional consultants sell architecture decks.
We deliver working pipelines.

Your data is supposed to drive decisions — not sit in a backlog waiting on a vendor’s sprint cycle. New Gig Data Engineering PODs bring senior architects and built-in QA to your platform from day one, without the enterprise consulting overhead.

We’ve run medallion migrations at Fortune 500 firms. We’ve built Unity Catalog governance from scratch. We’ve shipped Delta Live Tables pipelines at multi-terabyte scale. We’re not learning on your dime.

Why It’s Different

For teams done with
slow, expensive, low-QA data builds.

Senior Architects, Not Bench Filler

Lead architect is DP-203 Azure Certified with 10+ years across Deloitte, Capgemini, and enterprise-scale Databricks deployments. You get the person, not a staffing lottery.

QA Is Not an Afterthought

Every Data Engineering POD ships with dedicated QA — source-to-target reconciliation, automated testing, and regression frameworks built in from the start, not bolted on after the fact.

Hybrid Teams, Onshore Accountability

Onshore leads own the client relationship, the architecture decisions, and the delivery accountability. Offshore depth gives you scale without the coordination overhead.
Capabilities

Four areas. One team. All production-grade.

Our Data Engineering practice covers the full modern stack — from cloud-native

lakehouse builds to legacy ETL modernization to data quality frameworks that actually hold up at scale.

01 / Azure & Microsoft Fabric

Cloud-Native Data Platforms

From ADF orchestration pipelines to Microsoft Fabric Lakehouse builds, we design and implement Azure-native data infrastructure that’s built for production from day one.

Who it’s for: Organizations investing in Azure or Fabric who need senior engineers who’ve done this before — not a team still reading the docs.

Azure Data Factory
ADLS Gen2
Synapse Analytics
Microsoft Fabric
Logic Apps
02 / Databricks & Lakehouse

Medallion Architecture & Delta Lake

We architect Bronze-Silver-Gold medallion platforms, implement Unity Catalog governance, and build Delta Live Tables pipelines that power BI and ML workloads with confidence.

Who it’s for: Data and analytics teams scaling beyond legacy warehouses or migrating from Hive Metastore to Unity Catalog governance.

Databricks
Delta Lake
Unity Catalog
MLflow
DLT Pipelines
PySpark
03 / Legacy Migration

ETL Modernization & Cloud Lift

We’ve migrated SSIS to ADF, Hive to Unity Catalog, and on-prem SQL Server to cloud Lakehouse — repeatedly, at enterprise scale, with reconciliation frameworks that prove completeness.

Who it’s for: Engineering and IT leaders sitting on aging ETL infrastructure that’s blocking analytics, ML, or platform consolidation work.

SSIS to ADF
Snowflake
AWS Migration
Informatica
D365 F&O
04 / Data Quality Engineering

Validation, Testing & Trust

Our built-in QA capability means end-to-end validation frameworks, source-to-target reconciliation, and automated regression testing are part of the engagement — not a future phase.

Who it’s for: Analytics and finance teams who’ve been burned by pipelines that passed dev but broke production data — and leadership that needs to trust the numbers.

Source-to-Target Reconciliation
Automated Testing
Schema Validation
Data Contracts
The Team Model

Right-sized. Onshore-led.
Built for your scope.

We don’t hand you a random bench. Each Data Engineering POD is assembled for your engagement — onshore architects who own accountability, offshore depth that scales your capacity, and QA baked in throughout. Exactly what the work needs. Nothing it doesn’t.

  • Onshore architects hold the client relationship and technical direction
  • Offshore engineers provide delivery scale and cost efficiency
  • Dedicated QA analysts run validation from sprint one — never added later
  • POD size flexes with scope — no over-staffing, no thin benches
  • All engineers committed to your engagement — not split across five clients
Standard Data Engineering POD
2
Onshore Data Engineers
Architecture & client ownership

5
Offshore Data Engineers
Delivery depth & scale

2
Onshore QA Analysts
Validation & quality ownership

3
Offshore QA Analysts
Testing frameworks & regression

12 Engineers · Scales to Your Scope

10+

Years Senior-Led Experience

30–50%

Cost Savings vs. Large Firms

<9

Days from Signed SOW to Launch

TB+

Production Dataset Scale
How It Works

Simple model. No fluff, no fine print.

Data Engineering PODs are designed to be easy to explain to your CTO, easy to justify to your CFO, and easy to extend when the work earns it.

01

Define the Outcome

We start with your data goals — not a role list. Audit existing pipelines, align on platform requirements, and define what “done” looks like before we build anything.

02

Assemble the POD

We design a right-sized team with the exact technical profile your engagement requires. Senior architect included. QA included. No warmed-over resumes, no filler.

03

Rapid Launch

PODs plug into your tools, your repos, and your teams with defined milestones and check-ins. We move fast — from signed SOW to working pipelines in under nine days.

04

Measure & Scale

Extend, expand, or sunset cleanly. We benchmark performance, document everything, and keep the POD running only as long as it’s adding real value — not because a contract says so.

Ready to stop inheriting someone else’s pipeline mess?

Let’s talk about what your data platform actually needs — and build a POD around the outcome, not the org chart.