Sr. Engineering Manager / Coach, Data Team

Senior Engineering Manager / Coach, Data Platforms

Speridian Technologies is recruiting for a Senior Engineering Manager / Coach, Data Platforms, for the State of California Client, the Department of Healthcare Services, Behavioral Health. This person will be part of a long-term, fully budgeted, state-of-the-art, extremely vast technical modernization project working with a variety of cross-functional teams and stakeholders. This is a remote role; however, there will be meetings in the Sacramento area several times a year. Candidates are expected to work business hours, Monday-Friday, Pacific Time Zone.

Join DHCS's Behavioral Health Transformation : Where Purpose Meets Innovation

Location : Remote

Department : Department of Healthcare Services (DHCS), Behavioral Health Transformation (BHT)

Commitment : Full-Time Consultant (W2 employee of Speridian or 1099 / IC for Speridian)

At DHCS we are leading a transformative journey in Behavioral Health, reshaping systems and services to ensure better outcomes for communities across California. Our Behavioral Health transformation initiative is more than a projectits a movement to make California a leader in accessible, high-quality health services. We're setting the stage for a new era of government services built on agile methodologies, cutting-edge technology, continuous improvement, and a relentless commitment to serving the public good. At DHCS, we're looking for innovators who are passionate about purposeful work and excited by the opportunity to drive lasting change through innovative solutions.

Our Core Values : Achieve Together, Be Curious, Elevate Yourself, and Deliver Value

We achieve together by championing a team-oriented workplace built on mutual respect, collaboration, and open communication.

We encourage individuals and teams to constantly be curious and seek a deeper understanding and fresh ideas that drive innovation and meaningful change.

We provide a supportive workplace where you can elevate yourself and achieve personal growth through continuous learning, focused effort, and perseverance.

We deliver value as part of every action we take to serve California's citizens.

As a Senior Engineering Manager / Coach, Data, 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