Data Engineering
Reliable pipelines. Modern lakehouses. Streaming that scales.
A senior-led approach to data engineering.
We architect and operate the data foundation your teams depend on — from ingestion and transformation to serving and observability — across batch and streaming workloads.
- 40–70% fewer pipeline incidents
- 10x faster onboarding of new sources
- Unified batch + streaming architecture
- Trusted data across the org
Symptoms we're brought in to fix
If any of these feel familiar, you're not alone — and they're solvable.
Pipeline incidents
Data teams spend more time fighting fires than shipping.
Slow onboarding
New sources take months, not days, to make available.
Batch-only
The business needs minute-level data; pipelines run once a day.
No observability
Broken pipelines discovered by business users, not engineers.
A focused playbook, adapted to your stack
Every engagement combines these plays in the sequence that fits your context.
Lakehouse architecture
Delta, Iceberg, and Hudi on Databricks or Snowflake.
Streaming pipelines
Kafka, Kinesis, Flink, and Spark Structured Streaming.
Transformation & modeling
dbt-powered layered models and semantic layers.
Observability
SLAs, contracts, lineage, and alerting baked in.
Measurable impact on the numbers that matter
40–70% fewer pipeline incidents
10x faster onboarding of new sources
Unified batch + streaming architecture
Trusted data across the org
Fluent in the platforms you already run
We're pragmatic about tools — expert across the platforms that lead the industry.
A predictable path from kickoff to value
Discover
Workshops and audits to map your current state, stakeholders, and success metrics.
Design
Reference architecture, roadmap, and phased plan co-created with your team.
Build
A senior pod ships production-grade increments every two weeks.
Launch
UAT, cutover, training, and go-live with white-glove support.
Scale
Enablement, monitoring, and continuous improvement post go-live.
A typical 12-week engagement
Timelines flex with scope — this is what most first engagements look like.
Transparent, outcome-aligned engagement models
Choose the shape that fits your scope. Every plan includes senior practitioners — no juniors, no bench time.
A focused sprint to prove value on a single high-leverage use case.
- Discovery workshop
- One production use case
- 2–4 week engagement
- Async support
A senior pod embedded with your team for continuous delivery.
- Senior 3-person pod
- Bi-weekly demos
- Dedicated Slack channel
- Roadmap & backlog ownership
Multi-workstream program with governance, compliance, and change management.
- Multiple pods
- SOC 2 / HIPAA aligned
- Executive steering
- Global delivery
Common questions about data engineering.
Have a question that's not here? Our team is happy to help on a short call.
Book a consultationAdjacent practices that often pair with this one
Tell us about your data engineering goals.
Share a few details and a senior practitioner will reply within one business day — often with a first read on scope, timeline, and approach.
30-minute strategy call
Pick a time that works. We'll come prepared, share candid perspective, and leave you with two or three high-leverage next moves — whether or not we work together.
- Senior practitioner on the call
- Tailored to your stack and industry
- No pitch deck, no filler
- Follow-up recap within 24 hours