Principal Data Engineer
Redmond, WA, US
- Pay
- Pay not listedSimilar roles state $127K–$190K
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting
What you’ll work on
Full postingDesign and evolve ETL workflows, data pipelines and data models using Spark, Python and SQL
Build data solutions with Delta Lake, Parquet and secure APIs with RBAC access controls
Own complex system-design decisions, weighing scalability, resiliency, disaster recovery and cost
Establish monitoring, drive incident resolution and root-cause analysis for production systems
Automate safe deployments through CI/CD and provision resources with Infrastructure as Code
From the employer’s posting
Design and evolve ETL workflows, data pipelines, and data models using Spark, Python, and SQL
Build data solutions using Spark (or similar distributed systems), open table formats such as Delta Lake, and Parquet files; develop scalable, secure APIs and data access controls, including RBAC
Own complex system-design decisions, evaluate alternatives, and account for cross-team dependencies, scalability, resiliency, disaster recovery, and cost
Establish monitoring and operational readiness, drive incident resolution and root-cause analysis, and implement preventive improvements
provision resources through Infrastructure as Code; and deliver production-grade deployments through CI/CD
Tools in this posting
- C
- C#
- C++
- Java
- JavaScript
- Python
- SQL
- Databricks
- Delta Lake
- Spark
Source — Tool mentions in context
Lead data-platform architecture: Design and evolve ETL workflows, data pipelines, and data models using Spark, Python, and SQL. Drive hands-on, end-to-end implementation: Build data solutions using Spark (or similar distributed systems), open table formats such as Delta Lake, and Parquet files; develop scalable, secure APIs and data access controls, including RBAC; provision resources through Infrastructure as Code; and deliver production-grade deployments through CI/CD with observability and operational runbooks. Think beyond immediate requirements: Own complex system-design decisions, evaluate alternatives, and account for cross-team dependencies, scalability, resiliency, disaster recovery, and cost. Make evidence-based decisions: Proactively gather facts, validate assumptions, test hypotheses, and use telemetry and investigation to resolve complex problems rather than relying on unverified assumptions. Advance AI-native engineering: Apply AI across design, coding, testing, and troubleshooting; validate AI-generated outputs and help the team adopt effective, responsible practices. Own production outcomes: Establish monitoring and operational readiness, drive incident resolution and root-cause analysis, and implement preventive improvements. Automate safe deployments, targeting zero-touch deployment where possible. Raise engineering quality: Lead design and code reviews, strengthen testing, and embed security, privacy, and compliance into solutions throughout their lifecycle. Collaborate and mentor: Communicate trade-offs clearly, align partner teams on dependencies and ownership, mentor engineers, and share knowledge to improve team capabilities. Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Master's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 15+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience building data platforms for experimentation, metrics, and scorecards, including understanding success metrics, guardrail metrics, and data quality. Experience in Databricks like platforms.
Posting record
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- Live
- First seen by us
- Oct 5, 2026 · 25 days ago
- Recorded sightings
- 164
- Last seen by us
- Oct 30, 2026
- Employer says posted
- Sep 30, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
Lead data-platform architecture: Design and evolve ETL workflows, data pipelines, and data models using Spark, Python, and SQL. Drive hands-on, end-to-end implementation: Build data solutions using Spark (or similar distributed systems), open table formats such as Delta Lake, and Parquet files; develop scalable, secure APIs and data access controls, including RBAC; provision resources through Infrastructure as Code; and deliver production-grade deployments through CI/CD with observability and operational runbooks. Think beyond immediate requirements: Own complex system-design decisions, evaluate alternatives, and account for cross-team dependencies, scalability, resiliency, disaster recovery, and cost. Make evidence-based decisions: Proactively gather facts, validate assumptions, test hypotheses, and use telemetry and investigation to resolve complex problems rather than relying on unverified assumptions. Advance AI-native engineering: Apply AI across design, coding, testing, and troubleshooting; validate AI-generated outputs and help the team adopt effective, responsible practices. Own production outcomes: Establish monitoring and operational readiness, drive incident resolution and root-cause analysis, and implement preventive improvements. Automate safe deployments, targeting zero-touch deployment where possible. Raise engineering quality: Lead design and code reviews, strengthen testing, and embed security, privacy, and compliance into solutions throughout their lifecycle. Collaborate and mentor: Communicate trade-offs clearly, align partner teams on dependencies and ownership, mentor engineers, and share knowledge to improve team capabilities. Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Master's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 15+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience building data platforms for experimentation, metrics, and scorecards, including understanding success metrics, guardrail metrics, and data quality. Experience in Databricks like platforms.
Source details
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Redmond, WA, US
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