Every source, one contract.
Batch ETL, CDC, webhooks, SaaS connectors, event streams. Schema validation at the edge, typed contracts between producer and consumer.
Pipelines that
hold under load
AI without reliable data is a demo. Analytics without reliable data is fiction. We build the data foundation, pipelines, lakes, warehouses, streams and governance, so every model, dashboard and product feature reads from the same source of truth.
Capability map
Every layer a production system needs
A working data platform is six disciplines that have to cooperate. Miss one and the whole stack becomes unreliable, models degrade silently, dashboards drift, alerts fire false.
Lake, warehouse, or lakehouse.
Open table formats (Iceberg, Delta, Hudi) for flexibility, columnar warehouses for analytics. Cost-tiered, partitioned, queryable from ML and BI without copies.
SQL-first, tested, versioned.
dbt models with data tests, incremental materializations, documented lineage. Python where SQL can't go (ML features, custom parsing). Every transform is reviewable.
When hourly is too slow.
Kafka, Flink, Materialize for real-time pipelines. Event sourcing, stateful stream processing, exactly-once semantics where business logic requires them.
Trust in the data, measured.
Data contracts, anomaly detection, freshness SLAs, lineage tracking, catalog and PII masking. Compliance-ready (GDPR, HIPAA, SOC 2) without ceremony.
From warehouse to app.
Reverse ETL back into SaaS tools, feature stores for ML, low-latency APIs for products. The data gets to where it generates value, not just to a dashboard.
Reliability contract
SLOs we write into every platform
Data platforms degrade silently. We make failure loud, with measurable SLOs that page a human before the downstream consumer notices.
Freshness ≤ 15 min
Time from source event to queryable in warehouse, per critical table.
Completeness ≥ 99.5%
Rows landed vs rows emitted at source, measured per partition.
Schema stability 0 silent
Zero silent schema drift. Breaking changes blocked at PR time via contract tests.
Cost predictability ± 8%
Monthly warehouse / storage spend variance against forecast.
Stack
Tooling we default to
The shortlist below is where we start. Every engagement ends with a stack chosen for the problem, not for the logos.
01 Platforms
- Snowflake
- Databricks
- BigQuery
- AWS Redshift
- Iceberg + Trino
- ClickHouse
- Postgres
02 Orchestration
- dbt
- SQLMesh
- Dagster
- Airflow
- Prefect
- Temporal
03 Streaming & CDC
- Kafka
- Debezium
- Flink
- Materialize
- Redpanda
- Estuary
Adjacent disciplines
Data engineering connects everything
Most engagements pair data engineering with one of the disciplines below. Build order matters, data foundation first, model work second.
Next layer
Machine Learning Engineering
Training-ready data is upstream of every model. Feature store, labels, embeddings, then the ML team takes over.
UmbrellaArtificial Intelligence
Retrieval, agents, evaluation, inference, all lean on the data platform. The full AI discipline that sits on top of your pipelines.
AppliedComputer Vision
Image and video pipelines have their own storage and throughput profile, but they ride on the same data discipline.
Fast pathAI Integration
When the data is already clean and you just need AI wired in, skip the platform build and jump to integration.
Pipelines · lakes · warehouses
Build the foundation once, run it for years
Share the current state, sources, volume, target consumers and compliance profile. We respond with a gap analysis, reference architecture and build-order plan within ten days. Built to carry the AI workloads that arrive after the warehouse does.