Service
Data Engineering & Analytics
One number, one definition, one source of truth.Pipelines, warehouses and dashboards that turn scattered operational data into decisions your leadership team actually trusts.
Overview
The most expensive meeting in any company is the one where two teams present different numbers for the same metric and spend an hour arguing about whose spreadsheet is right. That is a data engineering problem, not a management problem.
We fix it at the source: get the data into one place on a schedule, define each metric exactly once in version-controlled transformation code, test the assumptions, and put the result somewhere everyone can see it.
Capabilities
What we build
Everything below is Data Engineering work we have shipped to production.
Data pipelines
Scheduled ETL and ELT jobs pulling from your databases, SaaS APIs, spreadsheets and third-party feeds, with retries, alerting and backfill built in.
Warehouse modelling
A dimensional warehouse on PostgreSQL, BigQuery or Redshift, with transformations in version control and every metric defined exactly once.
Business intelligence
Dashboards in Metabase, Superset or a custom console — designed around the decisions they support rather than around every column that happens to exist.
Product analytics
An event taxonomy designed before implementation, instrumented consistently across web and mobile, so funnels and retention cohorts mean something.
Geospatial analytics
PostGIS pipelines for location data, satellite imagery indices and coverage analysis — the machinery behind our own NDVI-based crop monitoring.
Data quality & governance
Automated freshness, volume and schema tests, lineage documentation, PII classification and retention policies aligned with the DPDP Act.
Technology
What we build it with
Storage
Processing
Presentation
Quality
Deliverables
What you get
- Documented pipelines running on a schedule with alerting
- Warehouse schema with a written metric dictionary
- Dashboards mapped to the decisions they inform
- Event taxonomy document and instrumentation in both apps
- Data quality test suite running automatically
- Access model and PII classification for compliance
Delivery
How the engagement runs
Same five stages every time, so you always know what week you are in and what comes next.
Discover
We start by understanding the business, not the feature list. Who is the user, what decision are they trying to make, what does success look like in numbers, and what is genuinely fixed versus assumed. Usually this reshapes the brief.
Design
Flows before pixels, then high-fidelity screens covering every state that matters — including the empty, offline and error states most projects discover late. Architecture is decided here too, and written down with its trade-offs.
Build
Two-week iterations, something in a real environment every single week, and a demo you can use rather than a status report you have to read. Typed contracts and CI keep the frontend and backend honest with each other.
Ship
Launch is a process, not a date: staged rollout, monitoring and alerting live before the first user, store submissions prepared against current policy, and a rollback path that has been tested rather than assumed.
Scale
After launch the questions change: what do users actually do, where does it cost too much, what breaks first at ten times the load. We instrument, review monthly, and plan the next quarter from evidence.
FAQ
Data Engineering questions
Related
Often paired with
AI & Machine Learning
AI agents, agentic workflows, chatbots, RAG systems, custom ML models and computer vision — engineered with evaluation, guardrails and cost control from the first line of code.
ExploreCloud & DevOps
Containerised infrastructure, CI/CD pipelines, monitoring and cost optimisation on AWS and Cloudflare — set up so a deploy is a non-event.
ExploreWeb Development
Marketing sites, customer portals, dashboards and full web applications — built to load fast on Indian networks and rank on day one.
ExploreLet us talk about your Data Engineering project.
Tell us the problem and the constraints. You will get an honest read on scope, timeline and whether we are the right people for it.