Data pipelines & platforms

Reliable ingestion, transformation, and modeling — the unglamorous substrate that makes reporting, analytics, and AI actually trustworthy.

What it covers

Batch and streaming ingestion from SaaS tools, databases, point-of-sale and device feeds, events, and files, with schema validation on the way in
Transformation and modeling in the warehouse or lakehouse — dbt on Snowflake, or Spark and Apache Iceberg on S3 — with tests and lineage
Migrations from legacy SQL ETL to a layered, reproducible model — medallion or otherwise — without a big-bang cutover
Lakehouse re-architecture: moving a warehouse-bound platform onto open table formats so storage, compute, and tools can change independently
Orchestration with Dagster or Airflow, plus the alerting, runbooks, and cost controls that keep it boring
Infrastructure as code and CI/CD for the platform underneath: Terraform, Kubernetes, GitHub Actions, and observability with Prometheus and Grafana
Document and knowledge corpora prepared for search, retrieval, and model training

How an engagement runs

We pick the dataset that is hurting most, make it reliable end to end, and use that path as the template for everything else. Small, reviewable changes that compound into a platform the team trusts.

What you get

Pipelines with tests, documentation, ownership, and monitoring — plus a modeling layer that downstream analytics and AI can depend on.

Book an intro call