- Manage and grow a team of Data Engineers, including goal-setting, 1:1s, feedback, hiring, and performance management
- Set architecture and design standards for ELT/ETL pipelines using Python, SQL, Spark, and Databricks
- Make build-vs-buy and platform trade-off decisions
- Drive dimensional modeling, star schemas, and incremental data patterns across the lakehouse and warehouse
- Set technical standards for streaming and event-data jobs, idempotency, and exactly-once semantics
- Ensure automated testing, anomaly detection, validation, lineage, metadata, and documentation
- Own SLOs and on-call rotations for data platforms
- Scale monitoring, alerting, capacity, and cost management
- Build roadmaps, sequence delivery, and drive measurable improvements in data freshness, completeness, and query performance
- Partner with Product, Data Science, and Application Engineering on source selection, feature readiness, experiment design, APIs, and semantic layers
- Lead design and code reviews, run brown-bag sessions, and coach engineers
Requirements
- 6+ years building and operating production data systems at scale
- Prior experience leading a team or mentoring senior engineers
- Experience managing people or leading ambiguous, high-stakes initiatives to clear results
- Experience with hiring, performance management, and career development
- Deep fluency with Python and SQL
- Expert knowledge of Spark, Databricks, and lakehouse patterns
- Strong data modeling skills
- Experience running workloads in AWS or a similar cloud, including storage, compute, networking basics, and cost controls
- Hands-on experience with Airflow, Databricks Workflows, or dbt
- Experience with CI/CD for data and infrastructure as code
- Ability to operate as both an individual technical contributor and a people manager
Core Competencies
Demonstrates expertise in managing and mentoring Data Engineers while setting architectural standards for ELT/ETL pipelines using Python, SQL, and Spark. Proven ability to drive data modeling, performance management, and collaboration across teams to enhance data systems.
Highest-signal resume keywords
- Data Engineering Leadership
- Python Proficiency
- SQL Expertise
- Spark and Databricks Knowledge
- AWS Experience
ATS Optimization Keywords
Hard Skills
- Data Modeling
- ELT/ETL Pipeline Design
- Dimensional Modeling
- Incremental Data Patterns
- Automated Testing
- Anomaly Detection
- CI/CD for Data
- Infrastructure as Code
- Performance Management
- Career Development
Soft Skills
- Team Management
- Mentoring
- Feedback Delivery
- Goal-Setting
- Collaboration
Industry Keywords
- Lakehouse Patterns
- Streaming Data
- Event-Data Jobs
- SLOs
- Cost Management
Tools & Technologies
- Databricks
- Airflow
- Databricks Workflows
- Dbt
- Monitoring Tools