Data engineering · Three Index
Data pipelines you can trust.
Ingestion, transformation and warehousing so product and analytics teams work from the same reliable sources — not spreadsheet exports.
- Team
- 50+ IT professionals
- Delivery
- Design, build, release, support
- Engagement
- Project or dedicated team
What this covers
Concrete work we take on under data engineering — not a catalogue of buzzwords.
Warehouse and lakehouse design
API and event-driven ingestion
Data quality checks and documentation
Problems we take on
If one of these sounds familiar, we have likely shipped something close — and we will say so if we have not.
Reports disagree because every team has its own export.
Batch jobs fail silently and nobody notices until Monday.
You need product features that depend on clean historical data.
Who this is for
This is for operations and product teams whose reports disagree, or whose product feature is blocked because history lives in exports. The buyer has usually appointed someone to “own the data” and discovered the problem is pipelines, definitions, and failure handling.
Analytics teams who already have a warehouse and need it hardened are a fit. A one-off dashboard with no shared definitions is a smaller job, and we will scope it that way.
How an engagement starts
Written scope
We list the sources, the questions the business actually asks, and the definitions that must be identical in every report. The written scope names the first datasets, the schedule, and the check that fails the job when a source is late or a total is implausible.
First increment
The first increment lands one source in your warehouse, documented, with a job that alerts when it fails. The next increment adds the joins and models downstream consumers were rebuilding by hand.
Decisions we make with you
Batch ELT is the right start when the business decides on yesterday’s data and the source can be pulled on a schedule.
Event streams are the right start when a product feature needs the fact within minutes and you are willing to operate the lag, the replay, and the duplicate handling.
We extend an existing warehouse when the model is sound and the pain is reliability. A new warehouse is the call when every team has a private extract and there is no system of record to repair.
What you own
You own the warehouse, the orchestration, the transform code, and the cloud account. Sources, schedules, and definitions are written down so an analytics hire can operate them.
- You own
- Warehouse
- You own
- Orchestration
- You own
- Transform code
- You own
- Cloud account
Where this shows up
The need shows up wherever several locations or several teams must agree on a balance, a stock count, or a day’s activity. The warehouse is the shared source; the screen is a consumer of it.
Further reading
Data pipelines that ops teams actually trust
Failure handling and ownership of pipelines.
ReadFintech ops dashboards without the theatre
What those pipelines are for, once a team has to run the day from them.
ReadTechnologies
Stacks we use when they fit the product and your team — not a mandatory toolkit for every engagement.
- Stack
- Python
- Stack
- Airflow
- Stack
- dbt
- Stack
- BigQuery
- Stack
- Snowflake
- Stack
- PostgreSQL
- Stack
- Kafka
How we work
Scope first, named engineers, biweekly demos, and you keep the repo.
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Scope before code
We write what is being built, what is excluded, integrations, dates and cost basis before the build starts. Changes get quoted before they get coded.
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Talk to the people doing the work
You meet named engineers and designers. There is no account manager translating technical questions in both directions.
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Working software every two weeks
Increments land on staging you can open and use. Progress you can click on beats progress in a status report.
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You own everything
Repository, cloud account, domains and credentials stay yours. We work in your accounts wherever possible.
FAQ
Answers specific to data engineering — stacks, engagement and what we actually ship.
Do you build dashboards?
We build the data layer that dashboards and products consume. BI tooling can be included or left to your analytics team.
Can you work with our existing warehouse?
Yes — we extend and harden what you have when that is the right move.
How do you keep data reliable?
Scheduled jobs with monitoring, quality checks and clear ownership so silent failures do not wait until Monday to surface.
Batch pipelines or real-time events?
Whichever the product needs. Many teams start with solid batch ELT and add event-driven paths where latency matters.
Who owns the warehouse and tooling?
You do. We build in your accounts and document sources, transforms and schedules so your team can operate them.
Related
Services that often sit beside data engineering on the same roadmap.
Why Three Index
AI-first software development company in Ahmedabad. Founded 2020. Fifty-plus IT professionals. Five hundred-plus projects shipped.
We are a software engineering company: large enough to staff serious products and small enough that the people who wrote a module can still explain it. See how we operate, our AI development work, browse case studies, read the FAQ, or join the team.
Tell us what you are trying to build.
Send a short description of the project. You will get a reply from someone technical — with questions worth answering, not a brochure.