Services/01
Data infrastructure that runs itself.
Most data platforms are held together by a heroic engineer and a cron job — or by a stack of SaaS subscriptions nobody fully controls. We design and build pipelines, warehouses, and integration layers that are observable, tested, and boring to operate — and that your team owns outright.
Signals
You probably need this if…
- 01Reports disagree with each other and nobody can say which number is right
- 02A single person owns the pipeline, and everything stops when they're out
- 03Nightly jobs run long, fail silently, or need manual restarts
- 04Every new data source is a multi-week integration project
- 05Your ETL/BI vendor's renewal pricing is scaling faster than the value it delivers
- 06You're paying warehouse bills that nobody can explain
What we build
Deliverables, not decks.
Pipeline architecture & build
Batch and streaming ingestion with retries, idempotency, and dead-letter handling designed in from day one — Airflow, Kafka, or whatever your scale actually requires.
Warehouse & modeling
Dimensional and wide-table modeling on Snowflake, Databricks, BigQuery, Redshift, or Postgres, with cost controls and query patterns tuned to how your business actually asks questions.
Custom connectors & vendor independence
First-class integrations for the APIs your vendors won't build — and owned replacements for the per-seat ETL tools holding your data hostage. Lock-in audits, exit plans, and the engineering to execute them.
Observability & data quality
Freshness SLAs, anomaly checks, lineage, and alerting, so you find out about bad data before your CFO does.
FAQ
Common questions
- What does a data engineering engagement with Aim include?
- Pipeline architecture and build, warehouse design and modeling, custom connectors, and data-quality and observability — delivered as working infrastructure your team owns outright, not a vendor subscription you rent.
- Do you require a specific data warehouse or work with our existing one?
- We build on whatever your scale actually needs — Snowflake, Databricks, BigQuery, Redshift, or PostgreSQL — and tune cost controls and query patterns to how your business really asks questions.
- How do you reduce data-vendor lock-in?
- We land raw data in storage you control before any vendor touches it, keep metric definitions and transformations in version-controlled code (dbt, SQL, Python), and engineer owned replacements for the per-seat ETL tools holding your data hostage — so every vendor is replaceable in a quarter.
→ Start a project
Have a data engineering problem on the roadmap?
Describe it in three sentences. We'll come back with how we'd approach it, what it likely costs, and whether we're the right team — usually within two business days.