Data engineering and analytics
Tankar builds the data layer between your operational systems and the people who need answers, including scheduled pipelines, a warehouse with a tested data model, dashboards, and forecasts where the history supports them. The same pipeline discipline runs TenderBazaar, which collects and normalises tender listings from Indian government portals.
From systems to answers
Operational systems produce data as a side effect. Turning it into answers takes pipelines that run on a schedule, a data model with one definition per metric, and dashboards people actually open. Tankar builds that layer and tests it like software.
Proof from our own products
TenderBazaar collects listings from Indian government portals, normalises them to one schema and serves search and alerts from the result. That pipeline discipline is what client data platforms get.
Governed, not ad hoc
Every metric is defined once and documented. Every pipeline run is tested. When a source changes, the tests fail before the dashboard lies.
Typical engagements
What this service usually produces, as deliverables rather than adjectives.
- Data warehouse and tested data model fed from your ERP, CRM, product database and spreadsheets
- Scheduled pipelines that collect, normalise and deduplicate data from external portals and APIs
- Management dashboards with definitions everyone agrees on and alerts on the numbers that matter
- Forecasting for demand, revenue or capacity with a documented accuracy record
- Migration from ad hoc reports and spreadsheets to governed, scheduled reporting
How it runs
The stages this service goes through, and what you see at each one.
Discovery
The questions the business needs answered, the systems that hold the data, and the definitions of each metric. Written estimate within 48 hours.
The written estimate follows within 48 hours of the scoped call.
Design
Source inventory, data model, pipeline schedule and the dashboard layouts, reviewed with the people who will use them.
Build
Pipelines with tests, the warehouse model, and dashboards built against real data. Weekly demo of live numbers.
Test
Reconciliation against source systems, data-quality tests on every run, and load checks on dashboard queries.
Launch
Scheduled runs with alerts on failure and drift, access control by role, and documentation of every metric.
Run
New sources and metrics, cost reviews of the warehouse, and a monthly report on pipeline health.
- Team shape
- A pod of a project manager, a data engineer, an analytics engineer and a QA engineer, with a designer for dashboards used by many people.
Stack for this service
The technologies this work is usually built on, and why each one is used here.
| Technology | Why we use it here |
|---|---|
| Data pipelines (Airflow, dbt) | Scheduled, tested, versioned transformations instead of scripts nobody dares to touch. |
| PostgreSQL | The warehouse for small and medium data volumes, with one database to secure and back up. |
| Warehouses and dashboards (BigQuery, Snowflake, Metabase, Power BI) | A cloud warehouse when volume or concurrency outgrows PostgreSQL; Metabase or Power BI for dashboards the whole company can use. |
| Python and FastAPI | Collectors, normalisers and forecasting models in the language the data ecosystem is built in. |
| GitHub Actions | Pipeline changes are tested and deployed like application code. |
Selected work
Case studies where this service carried the engagement. Only outcomes with a source are shown.
Own productGovernment and public sectorTenderBazaar: tender discovery and alerts for Indian SMEs
A tender discovery platform that collects listings from central, state and municipal portals into one searchable feed with daily alerts.Services: SaaS product development, Mobile app development and 1 moreRead the case study
Own productTravel and hospitalityTripBNG: a B2B travel booking platform for agents
A booking platform that gives travel agents supplier inventory, quotes, credit limits and settlement reports in one account.Services: SaaS product development, API development and integrations and 1 moreRead the case study
Engagement models that fit
A first warehouse and dashboard set is usually fixed price; new sources and metrics run on a retainer.
- Fixed priceA defined scope with a clear end state: an MVP, a website, a well-specified module or an integration.
- Time and materialsEvolving products, research-heavy work such as AI features, and engagements where the backlog is set sprint by sprint.
- Retainer and supportLive products that need maintenance, monitoring, small improvements and a response commitment after launch.
Questions buyers ask
What buyers ask most about this service, answered before the first call.
Tell us what you are building.
NDA on request. Written estimate within 48 hours of a scoped call. Reply within one business day.