Data Engineers
Data engineers for teams that need the data layer to stop being fragile
Silicon Development helps product teams add data engineers who can design pipelines, build warehousing and analytics infrastructure, and support the production systems that reporting and product decisions depend on. This role matters when bad data, brittle pipelines, or slow reporting are starting to affect product and business decisions.
What this role usually owns
These are the kinds of problems this role should be able to take responsibility for inside an operating product team.
Building and maintaining ETL or ELT pipelines that reporting and product teams rely on
Standing up or scaling warehousing and analytics infrastructure
Designing streaming and real-time data systems for production workloads
Implementing data quality, governance, and compliance controls
When teams usually need this role
- Reporting, analytics, or product instrumentation is blocking decisions because the data layer was never built to scale
- You need someone who has already worked in regulated or compliance-heavy data environments, not someone learning on your systems
- You have been trying to hire a data engineer locally for months and the right candidates are still not available
What we screen for
- Practical pipeline design and failure handling, not just tool keyword familiarity
- Judgment around data quality, observability, and operational reliability
- Ability to work across engineering, analytics, and product stakeholders without creating translation overhead
Related case studies
A few examples of the environments and outcomes this role supports.
BioPharma
BioPharma AI Platform
A biotech company needed to turn RNA splicing research into a commercial SaaS platform for pharmaceutical clients. SD embedded a 10-person team to build it.
Read case study →Consumer Tech / Data Platform
Pragma.AI
Serving as the primary engineering team for Pragma.AI
Read case study →Healthcare Data & Analytics
Healthcare Analytics Platform
A 6+ year embedded engineering partnership supporting clinical quality measures, platform modernization, and long-term delivery continuity in a regulated healthcare environment
Read case study →Data work gets expensive fast when the foundation is weak
The right data engineer should make reporting, instrumentation, and pipeline reliability feel less fragile, not more complicated.