Data engineering and analytics
Pipeline design, ETL/ELT development, warehouse architecture, and BI reporting across modern stacks.
Reporting that depends on one person and a spreadsheet
A lot of mid-market reporting runs on a process nobody wrote down: an export from one system, a lookup against another, an hour of manual cleanup before the numbers are trustworthy enough to present. It works until the person who built it leaves, or the data volume outgrows what a spreadsheet can hold.
The fix isn't always a big-platform migration. Sometimes it's a properly built pipeline into a warehouse that already exists but never got used correctly. Sometimes it genuinely is time for a new platform. We start by finding out which.
Good data engineering makes the report boring: it runs on a schedule, it doesn't depend on one person's laptop, and the numbers are the same whether the CFO or an intern pulls them.
What's included
- Pipeline designETL or ELT processes that move data reliably from source systems to where it needs to be, on a schedule, without manual intervention.
- Warehouse architectureSchema design that supports the reports you actually need, not a generic model that fights every query.
- BI reporting and dashboardsPower BI, Tableau, or Looker implementations built around the decisions leadership actually makes.
- Data quality and validationChecks that catch a broken pipeline before it produces a wrong number that gets presented in a board meeting.
- Legacy report migrationMoving the spreadsheet-dependent reports everyone relies on into something that survives someone leaving.
- DocumentationPipeline and schema documentation that means the next person doesn't start from zero.
How the engagement runs
- 01Data estate assessmentWhere the data lives, how reports get built today, and where the process is most fragile. Often the right starting point is our Data Platform Assessment.
- 02Target designA pipeline and warehouse architecture that fits your actual reporting needs and team skills.
- 03BuildImplementation in phases, starting with the report or process causing the most pain.
- 04HandoffDocumentation and training so your team can extend the pipeline without calling us for every new report.
Technologies and platforms
- Snowflake
- Databricks
- PostgreSQL
- Power BI
- Tableau
- dbt
- Azure Data Factory
Proof
The same rigor we bring to database administration, real evidence over guesses, applies here: a pipeline that runs and a number that's trustworthy the first time, not after three rounds of double-checking.
Common questions
Do we need a full data warehouse, or would something simpler work?
Often something simpler works, especially early on. A well-organized Postgres database with good pipelines can outperform an over-built warehouse nobody has time to maintain.
Which BI tool do you recommend?
Power BI for Microsoft-centric teams, Tableau or Looker otherwise. The choice usually follows what your team already knows, not the other way around.
Can you fix a specific report instead of the whole pipeline?
Yes. Many engagements start with the one report causing the most pain and expand from there once the pattern proves out.
How does this relate to the AI work you do?
Clean, reliable data is the prerequisite most AI projects skip. Estates that need both usually start here.
Related services
Bring the report that takes someone two days a month to build.
That is usually exactly where a pipeline pays for itself.