Senior Data Engineering Manager/Coach
New
J
JobgetherHealthcare data
Fully remote U.S. opportunity, Working hours aligned with Pacific Time; Monday through Friday.Full-TimeManager
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLKafkaSnowflakeAirflowData engineeringSparkdbtDatabricks
Requirements
- Proven experience managing large data engineering teams, ideally 20+ members.
- Experience owning budgets, P&L responsibilities, or financial accountability for data platforms or technology products.
- Demonstrated ability to connect data infrastructure investments with business outcomes, KPIs, and ROI.
- Experience building and operating production-scale data platforms across the full data lifecycle, from ingestion through consumption.
- Strong understanding of modern data engineering practices, cloud data technologies, data architecture, governance, and quality management.
- Experience making architectural decisions involving data pipelines, platforms, warehouses, lakes, and streaming systems.
- Hands-on knowledge of Snowflake, Databricks, BigQuery, Redshift, or comparable technologies.
- Experience with Apache Spark, Airflow, dbt, Kafka, and real-time or streaming architectures.
- Familiarity with AWS, Azure, or GCP data services and infrastructure-as-code practices.
- Strong understanding of dimensional modeling, data vault, and data mesh principles.
- Proficiency with SQL, Python, Scala, or comparable data-focused programming technologies.
- Strong financial and operational skills, including cloud cost optimization, capacity planning, vendor evaluation, and resource management.
- Demonstrated success in performance management, career development, hiring, team building, and difficult conversations.
- Bachelor's degree in Computer Science, Engineering, or equivalent professional experience.
- Ability to work Monday through Friday on Pacific Time and travel to Sacramento approximately 10% of the time.
Responsibilities
- Define data platform strategy and make architecture decisions across enterprise data systems, pipelines, warehouses, and lake architectures.
- Design and oversee scalable batch and real-time data solutions while balancing freshness, accuracy, reliability, performance, and infrastructure costs.
- Establish engineering standards for data quality, governance, documentation, monitoring, security, and compliance.
- Own platform budgets, cloud data costs, tooling investments, resource planning, and ROI analysis for data initiatives.
- Manage, mentor, and develop a team of approximately 10–20+ data engineers through coaching, career planning, performance management, hiring, and succession development.
- Partner with Analytics, Data Science, Business Intelligence, Product Management, and Software Engineering teams to define requirements and prioritize data products.
- Drive continuous improvement across ETL/ELT processes, platform tooling, data reliability, pipeline performance, and operational efficiency.
- Support platform migrations and technology adoption while guiding teams through organizational and technical change.
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