Senior Data Engineering Manager/Coach

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OtherData Engineering Manager

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Quick Summary

Key Responsibilities

Teams deliver scalable data platforms that enable analytics and data-driven decision making2.

Requirements Summary

Strong partnerships enabling data democratization and self-service analytics6.

Technical Tools

OtherData Engineering Manager

Job Title: Data Engineering Manager, Data Platform – Behavioral Health Transformation
Location: Remote (Working hours are Monday-Friday PST)
Division: Medical Enterprise System Modernization Division (MESMD)
Department: California Department of Health Care Services (DHCS)
Must work on shore in the US. We cannot sponsor or transfer an H1B Visa for this role
Remote Role- USA, with some travel- about 10% to Sacramento for key meetings
Working hours are Monday-Friday, PST

Commitment: Full-Time Consultant to DHCS; you can work with us two ways: as a W2 employee of Speridian OR a 1099/IC/LLC. This is a long-term contract Speridian has with DHCS. We are in year three of a three-year contract with two two-year extensions.

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
Required Qualifications

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

Business & Financial

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

What Sets Top Performers Apart
Success in this role goes beyond compensation, work-life balance, or a typical corporate career mindset. We’ve found that intrinsic motivation—a genuine drive to grow, solve complex problems, and create lasting impact—is a defining trait of those who truly thrive here.
We’re looking for individuals with a consulting mindset, a passion for technology, and above all, a deep personal commitment to delivering meaningful results. This environment is fast-paced and demanding—we hold ourselves to high standards every day.
This role isn't for everyone, and we’re honest about that. But for those motivated by purpose, challenge, and the chance to lead with impact, it could be the ideal next step in your career.

Interview Process:
Recruiter Call and then:

Take-home project (1 hour)
1 hour (Take Home Review, People Management, Culture & Communication)

1 hour (Technical, Design)
45 minutes (Executive & Critical Thinking)

Speridian Technology is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Reasonable accommodations are available for qualified individuals with disabilities during the application process.