Principal Data Engineer
BayRockLabs Open · verified Oct 24, 2026 At a glance
Senior Data Engineering role at BayRockLabs. Milpitas · full-time · no salary stated · we estimate $142,500–$193,650 across the United States.
BayRockLabs has not published a salary for this role. The figure below is our estimate, not theirs.
dataroles summary, based on the employer's posting.
What you'll do
- Design Azure and Databricks platforms that scale securely, perform well, and control cloud costs
- Build batch and streaming pipelines with PySpark, Spark, and Delta Lake for enterprise data workloads
- Create warehouse models, metadata-driven ingestion, and change-data capture across source systems
- Deliver data products, semantic layers, and reporting foundations aligned with business needs
- Lead architecture discussions, plan technical roadmaps, and guide teams on governance and delivery
What you bring
- At least five years in hands-on data engineering or data architecture
- Strong working expertise across Azure and Databricks
- A track record building scalable, secure, high-performance data platforms
- Ability to steer architecture decisions, lead design conversations, and mentor colleagues
Who this fits
This principal-level role suits a hands-on data engineer or architect ready to shape platform design and guide other engineers. The position is based in Milpitas, California.
From the employer
About BayRock Labs
At BayRock Labs, we pioneer innovative tech solutions that drive business transformation. As a leading product engineering firm based in Silicon Valley, we provide full-cycle product development, leveraging cutting-edge technologies in AI, ML, and data analytics. Our collaborative, inclusive culture fosters professional growth and work-life balance. Join us to work on ground-breaking projects and be part of a team that values excellence, integrity, and innovation. Together, let's redefine what's possible in technology.
We are looking for a highly skilled Senior Data Engineer / Data Architect with deep hands‑on expertise in Azure, Databricks, PySpark, Python, SQL, and modern data warehousing. This role is ideal for someone who has built large‑scale data platforms, designed enterprise‑grade architectures, and delivered high‑quality data products in cloud environments.
Core Technical Expertise
Azure Cloud Platform
- ADLS Gen2, ADF, Synapse, Azure SQL, Key Vault, App Services
- Cloud‑native architecture design, scalability, and cost optimization
- Security & governance: RBAC, Managed Identity, Private Endpoints
Databricks Engineering
- End‑to‑end development on Azure Databricks
- Lakehouse architecture implementation
- Delta Live Tables (DLT), Unity Catalog
- Performance tuning, cluster optimization
- CI/CD for Databricks workloads
Big Data & Processing Frameworks
- Apache Spark with advanced PySpark transformations
- Structured Streaming & batch data pipelines
- Delta Lake architecture & optimization
Programming & Querying
- Python for automation, data engineering, and orchestration
- PySpark for distributed data processing
- SQL for complex query optimization and analytics engineering
Data Architecture & Engineering
- Enterprise Data Warehouse (EDW) architecture
- Dimensional modeling (Star/Snowflake)
- Data Vault 2.0 modeling
- Metadata‑driven ingestion frameworks
- Change Data Capture (CDC) implementation
- Medallion architecture (Bronze/Silver/Gold)
- Data lineage, governance, cataloging
- Master Data Management (MDM)
Data Products & Analytics Enablement
- Design and delivery of scalable data products
- Business‑aligned semantic layer design
- KPI frameworks & enterprise reporting enablement
- Integration of ERP, SaaS, and operational systems
- Hybrid Lakehouse + EDW architectures
DevOps & Engineering Practices
- CI/CD pipelines (Azure DevOps, GitHub Actions, Bitbucket)
- Infrastructure as Code (Terraform, ARM templates)
- Automated testing (unit, integration, data quality)
- Monitoring, logging, and observability
- Agile/Scrum delivery
Strategic & Leadership Impact
- Principal‑level solution architecture design
- Cross‑functional stakeholder collaboration
- Technical roadmap planning and execution
- Guidance on data governance frameworks (GDPR, SOX)
- Cloud cost optimization strategies
- Migration from legacy EDWs (Teradata, Oracle, SQL Server) to Lakehouse
Ideal Candidate Profile
- 5–10+ years of hands‑on experience in data engineering or data architecture
- Strong expertise in Azure + Databricks ecosystem
- Proven experience building scalable, secure, high‑performance data platforms
- Ability to lead design discussions, influence architecture decisions, and mentor teams
- Strong problem‑solving mindset and passion for modern data engineering
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