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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineering Manager - **Company:** jazzhr - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, BigQuery, Cloud Computing, Cloud Database, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Data Vault Modeling, Decision Support Systems, Dimensional Modeling, Python (Programming Language), Operational Databases, Data Mesh, Application Data, Scala (Programming Language), Software Engineering, SQL Databases, Data Processing, Enterprise Software Applications, Snowflake, Apache Spark, Event Driven Architecture, Information Technology, Data Analytics, Apache Kafka, Data Management, Data Pipelines, Amazon Redshift, Databricks - **Published:** October 1, 2026 - **Apply:** https://speridiantechnologies.applytojob.com/apply/HWM4PEmvEn/Senior-Data-Engineering-ManagerCoach?source=GS ## About the Role * Proven track record managing data engineering teams of 20+ members * Experience owning P&L or budget responsibility for data platforms or products * Demonstrated ability to connect data infrastructure to business outcomes and ROI * Experience building and operating production data platforms at scale * Strong background in modern data engineering practices and cloud data technologies * Demonstrated ability to make architectural decisions for data systems and pipelines * Experience with full data lifecycle from ingestion through consumption * Track record of developing data engineers and building strong data engineering cultures * Bachelor's degree in Computer Science, Engineering, or equivalent experience Technical * Data Platforms: Snowflake, Databricks, BigQuery, Redshift, or similar * Data Processing: Apache Spark, Airflow, dbt, Kafka, streaming architectures * Cloud & Infrastructure: AWS/Azure/GCP data services and infrastructure as code * Data Modeling: Dimensional modeling, data vault, data mesh principles * Languages: SQL, Python, Scala, and data-specific programming paradigms ## Description Ready to democratize data for California's healthcare revolution? Join the Department of Healthcare Services (DHCS) as a Data Engineering Manager, where you'll architect the data platforms that power evidence-based decisions affecting 16 million Californians' healthcare journeys. As a Data Engineering Manager, you'll lead a team of data engineering experts building enterprise-scale data platforms that transform petabytes of healthcare information into actionable insights. This role goes beyond traditional ETL pipelines - you'll design real-time streaming architectures, implement advanced analytics capabilities, and create self-service data products that empower teams across the organization. Your data platforms will enable predictive analytics that prevent fraud, optimize care delivery, and literally save lives through better healthcare outcomes. DHCS offers the rare opportunity to work with healthcare data at a scale that rivals major tech companies, while directly improving public health. You'll have full ownership of the data platform strategy, invest in modern tools like Databricks and Snowflake, and the mandate to build a world-class data engineering organization. Our commitment to data-driven transformation means your work will be highly visible and directly tied to the department's strategic objectives. We're looking for a data platform visionary who understands that great data engineering enables great decisions - someone who can optimize Spark jobs while evangelizing data literacy, who treats data quality as sacred, and who believes government should lead in leveraging data for public good. Responsibilities & Outcomes 1. Data Platform Leadership & Architecture * Drive data platform strategy and architecture decisions for enterprise data systems * Design and oversee data pipelines, warehouses, and lake architectures * Champion data engineering best practices including data quality, governance, and documentation * Make critical technical trade-off decisions balancing data freshness, accuracy, and infrastructure costs Outcome: Teams deliver scalable data platforms that enable analytics and data-driven decision making 2. Business Ownership & Financial Accountability * Own business metrics and ROI for data platform investments and initiatives * Develop and track cost-benefit analyses for data infrastructure and tooling decisions * Manage team budget including cloud data costs, tooling, and infrastructure spend * Translate data engineering work into business value and analytical capabilities for stakeholders * Drive efficiency improvements in data processing costs while maintaining data quality Outcome: Data engineering decisions driven by business value with clear ROI and financial accountability 3. People Management & Development * Manage, mentor, and develop a team of 10-20 data engineers * Conduct regular 1:1s focused on career development and performance * Execute performance management including promotions, improvement plans, and difficult conversations * Build diverse, inclusive teams through thoughtful hiring and team composition Outcome: High-performing teams with strong retention, clear growth paths, and engaged data engineers 4. Data Engineering Excellence & Quality * Establish and maintain standards for data quality, pipeline reliability, and monitoring * Drive continuous improvement in ETL/ELT practices and data tooling * Ensure appropriate data governance, security, and compliance implementation * Implement metrics and monitoring for data pipeline performance and data quality Outcome: Consistent delivery of reliable, high-quality data products with minimal pipeline failures 5. Cross-functional Partnership * Partner with Analytics, Data Science, and Business Intelligence teams on requirements * Collaborate with Product Management on data product roadmap and prioritization * Work with Software Engineering teams on application data integration * Communicate data architecture concepts and trade-offs to non-technical stakeholders Outcome: Strong partnerships enabling data democratization and self-service analytics 6. Talent Strategy & Team Building * Lead technical interviews and hiring decisions for data engineering roles * Develop team skills through mentoring, training, and stretch assignments * Identify and cultivate future data platform leaders * Build team culture emphasizing data quality, automation, and continuous learning Outcome: Strong talent pipeline with data engineers growing into senior and leadership roles, * Financial Management: Cloud data cost optimization, budget ownership, and ROI analysis * Business Metrics: Defining and tracking data platform KPIs and usage metrics * Value Communication: Articulating data investments in business terms * Resource Planning: Capacity planning for data workloads and storage * Vendor Management: Evaluating and managing data tools and platform services Leadership * People Management: Performance management, career development, and difficult conversations * Team Building: Hiring, onboarding, and creating inclusive team environments * Communication: Technical and non-technical stakeholder management * Decision Making: Data-driven decisions balancing multiple constraints * Strategic Thinking: Aligning data platform efforts with organizational goals * Change Management: Leading teams through platform migrations and tool adoptions General * Problem-Solving: Complex data and organizational challenge resolution * Collaboration: Working effectively with Analytics, Data Science, and Engineering functions * Mentorship: Developing junior and senior data engineers * Process Improvement: Identifying and implementing efficiency improvements * Business Acumen: Understanding business context and impact of data platform decisions ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Making Data Warehouses fast. 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