Snowflake / Databricks / BigQuery
Lakehouse architecture, warehouse modeling, role-based access, resource monitors, query optimization, FinOps for compute.
Senior engineering · Data Engineering
Senior Data Engineering engineering for production systems.
Production data engineering: Snowflake, dbt, Airflow / Dagster, lakehouses, data quality, lineage, and the governance frame that distinguishes a working warehouse from a data swamp.
- In production
- 2018+
- Senior bench
- 22+ G6/G9 engineers
- Floor
- G6+ on client work
- Engagement
- Outcome-led, not hourly
Why senior, not contractor
Data Engineering in production is a different problem than Data Engineering on a laptop.
Most data warehouses in production today are a graveyard of one-off SQL, Airflow DAGs nobody owns, and dashboards built against tables that broke last quarter. The data substrate every AI use case eventually rests on either earns its keep or it doesn't. Prosigns ships data engineering as production substrate: dbt-modeled with tests in CI, Airflow / Dagster orchestration with proper failure semantics, data quality monitoring, lineage that surfaces in Slack when something breaks, and a governance frame that procurement actually trusts.
Senior floor
G6+ minimum
Bench depth
22+ G6/G9 engineers
In production
2018+
Engagement
Outcome-led SOW
Where Data Engineering ships
6 sub-areas, grounded in shipped engagements.
Specific applications of Data Engineering we’ve built and operate. Every example below maps to a real engagement, not a bullet on a stack-card.
01 02 dbt-modeled warehouses
dbt Core / Cloud with proper layered models (staging / intermediate / marts), tests, documentation, exposure tracking, lineage in CI.
03 ELT orchestration
Airflow, Dagster, Prefect, Mage. Proper retry semantics, alerting, lineage, and idempotent task discipline.
04 Data ingestion
Fivetran / Airbyte / Stitch for source connectors, CDC pipelines for low-latency replication, custom Python/Go ingestion where needed.
05 Data quality + observability
Great Expectations, dbt tests, Monte Carlo / Soda for quality monitoring. SLAs on data freshness, completeness, distribution.
06 Streaming + real-time
Kafka + Flink for streaming pipelines, Materialize / RisingWave for streaming warehouses, ClickHouse for real-time analytics.
Stack depth
The Data Engineering ecosystem, owned.
Frameworks, libraries, and runtime tools the bench has shipped in production. Not a CV-skim, a working depth.
Warehouses + lakehouses
- Snowflake
- Databricks
- BigQuery
- Redshift
- Microsoft Fabric
Modeling
- dbt Core
- dbt Cloud
- SQLMesh
- Hex
- Coalesce
Orchestration
- Airflow
- Dagster
- Prefect
- Mage
- Modal
Ingestion
- Fivetran
- Airbyte
- Stitch
- Hevo
- Custom Python/Go
Quality + governance
- Great Expectations
- Monte Carlo
- Soda
- Datafold
- Atlan
Engagement models
Three ways to engage Data Engineering engineers.
We don’t bill hourly contractors. Engagements run against outcomes, choose the shape that matches the work.
See engagement models- 01
Fixed-scope
Defined deliverable, fixed price
When the deliverable is clear and the scope is bounded: an MVP, a migration, a discrete platform build. Senior engineering against a written outcome, not against a body count.
- 02
Embedded squad
Senior product team, ongoing
When the work is product-shaped and the cadence is continuous. A senior pod (engineering + design + PM as needed) embedded into your team, with the practice lead co-piloting from HELM.
- 03
Managed services
Steady-state operations
When the system is running and needs ongoing engineering ownership: operations, SLO defense, release management, security and compliance evidence. Monthly retainer against a published SLA.
Selected work
Data Engineering engineering that shipped.
Retail
Snowflake + dbt warehouse build for a 9-region D2C retailer replacing Looker → unmanaged SQL chaos.
94%fewer dashboard breakagesModeled the warehouse in dbt with staging / intermediate / marts layers, full test suite in CI, and exposure tracking. Airflow → Dagster for orchestration. Monte Carlo for data-quality monitoring. SLAs published per dataset.
Duration · 5 months
Brief us
Reply < 4 business hoursHave a Data Engineering workload? Send a brief.
Five fields. Goes straight to the practice lead, not an SDR. We’ll reply with a senior engineer’s read on fit, scope, and the engagement model that suits the work.
FAQ
Questions buyers ask.
Everything below also appears in the proposal and the SOW: no surprises after signing.
Snowflake, Databricks, or BigQuery?
Snowflake for SQL-led warehouses where elasticity and ergonomics dominate. Databricks for ML/data-science workloads where the lakehouse + Spark + MLflow story is the value. BigQuery for organizations already on GCP or where the serverless economics fit. All three are in our portfolio; we’ll tell you which fits the workload.
dbt, is it really enough governance?
dbt with proper testing, documentation, exposure tracking, and lineage covers most mid-market governance needs. For regulated environments (healthcare, financial services) we add dedicated data-catalog tooling (Atlan / Collibra), data-quality monitoring (Monte Carlo / Soda), and explicit SLA dashboards. dbt is the spine, not the whole story.
Is this staff-aug, or is it engineering-led delivery?
Engineering-led delivery. We don't bill hourly contractors against your JIRA board. Every engagement runs against a defined outcome with a senior engineer accountable from kickoff to operating cutover. If you genuinely need staff-aug, discrete bodies, your management, hourly rates, we'll be honest and route you to a partner that fits.
What seniority floor are we talking about?
G6 minimum (six-plus years in their craft) on every billable hour. Department leads are G9 or G10. We don't flex juniors onto the bench mid-sprint, we don't subcontract to delivery centers, and we don't dilute senior rates with mixed staffing. The bench in the proposal is the bench in production.
How does pricing work?
Three engagement models published at /engagement-models/. Fixed-scope for defined deliverables, embedded squads for ongoing product work, managed services for steady-state operations. Rates depend on seniority, engagement length, and region. Discovery + scoping conversation is free; SOWs are written against deliverables, not bodies.
Where are your engineers based?
Senior-only across Dallas, Doha, Lahore, and Islamabad. We staff against the engagement's needs (timezone, language, regulatory frame), not against arbitrary regional preferences. Most engagements run with a US/EU-aligned core and a follow-the-sun extended bench when the workload warrants it.
Will the engineers I see in the proposal be the engineers shipping the work?
Yes. We name the engineers in the SOW, attach their profiles, and they're on the kickoff. We don't bait-and-switch with senior reviewers and junior execution. If a named engineer needs to roll off the engagement (rare), we surface a replacement from the same seniority tier with explicit handoff.
Talk to a Data Engineering lead
Senior Data Engineering engineering, accountable from kickoff.
Bring the workload: we’ll bring a senior engineer plus the practice lead most relevant to the work. 30 minutes, no obligation, no junior reps.