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Services
Data Engineering Services for Reporting and AI
We build the pipelines and warehouse that pull data from your product, CRM, payments and spreadsheets into one place with agreed definitions. Your reports start to match, and your data is ready for the AI features you want to build next.
Where we help
Data problems we fix
Most of them come down to data living in too many places with no agreed definitions.
Two reports, two different numbers
Sales and finance count revenue differently and meetings start with arguments about which figure is right. We define each metric once, in code, and every dashboard reads from it.
Reporting is a manual export every Monday
Someone downloads CSVs from five tools and rebuilds the same spreadsheet. We automate the loads so the numbers are ready before anyone logs in.
Pipelines break without anyone noticing
A source changes a field name and a dashboard is wrong for two weeks. We add tests and alerts so failures are caught the day they happen.
Your data is not ready for AI
You want an assistant or prediction feature, but the data is spread out, duplicated and undocumented. We clean, join and document it so a model has something reliable to work with.
What we build
What a data engagement delivers
Everything is version-controlled, tested and documented, so your team can run it after we leave.
Data pipelines
Scheduled and event-driven loads from your databases, SaaS tools and files into a central store, with retries and alerts.
Data warehouse or lakehouse
A warehouse sized to your volume and budget, from PostgreSQL for small teams to Snowflake, BigQuery or Databricks for larger ones.
Data models and metric definitions
Tested dbt models that turn raw tables into clean customers, orders and revenue tables, with each metric defined once.
Dashboards and reporting
Dashboards in the BI tool your team prefers, built on the shared models so every report agrees.
AI-ready datasets
Cleaned, chunked and indexed documents and records for search, RAG and agents, with access rules carried through from the source.
Data quality monitoring
Checks for freshness, volume, duplicates and broken relationships, with alerts sent to Slack or email.
Process
How we build your data platform
We start with the questions the business needs answered and work back to the data.
Audit sources and questions
We list the decisions you need data for, the systems that hold it and where numbers disagree today. You receive a source map and a prioritized list of metrics.
Design the platform
We choose the warehouse, loading tools and model structure for your size and budget. You receive an architecture document and a cost estimate for the tools.
Build the first pipeline end to end
One source, through the models, to one working dashboard. You receive a live report your team can check against the numbers they trust.
Expand and test
We add the remaining sources and metrics in weekly batches, each with tests. You receive new dashboards and data documentation every week.
Hand over
We train the people who will own the platform and document how to add sources and fix failures. You receive the runbook, access to everything and a recorded walkthrough.
Tech stack
Data tools we work with
We prefer managed, widely used tools with a sensible cost at small volumes.
- Ingestion
- AirbyteFivetrandltApache Kafka
- Storage
- PostgreSQLSnowflakeBigQueryDatabricksDuckDB
- Transformation and orchestration
- dbtApache AirflowDagsterPython
- BI and analytics
- MetabaseLooker StudioPower BI
- AI data
- pgvectorPineconeQdrant
Data engineering FAQ
Do we need a data warehouse?
If you report from more than two or three systems, or reports regularly disagree, a warehouse usually pays for itself. For small volumes a managed PostgreSQL database is often enough, and we will recommend that when it fits.
What will the tools cost us each month?
It depends on data volume and how often it refreshes. The architecture document includes an estimate of monthly tool and cloud costs, and we design to keep them predictable.
Can you work with our existing data setup?
Yes. We often start by fixing and documenting what exists rather than rebuilding it. We replace a tool only when it is the cause of the problem.
How do you handle sensitive and personal data?
We load only the fields you need, mask or exclude personal data where possible and keep it in your own cloud accounts. Access is role-based, and we follow the data protection rules that apply to you, such as India's DPDP Act or GDPR.
How soon will we see a working dashboard?
The first end-to-end pipeline and dashboard is usually live within three to four weeks of the start. Further sources are added in weekly batches.
Who maintains it after you hand over?
Your team, us, or both. We document everything and train the people who will own it, and we can stay on a small monthly retainer if you prefer.
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Next step
Talk to us about Data Engineering
Book a free 30-min call. Tell us what you need and we will suggest a practical first step, or tell you honestly if we are not the right fit.