Senior Data Engineering Leader/Coach

Speridian Technologies is hiring a Data Engineering Leader to support the California Department of Health Care Services' behavioral health transformation. The role leads enterprise-scale data platform architecture, manages a data engineering team, oversees budgets and ROI, and partners with analytics, data science, business intelligence, product management, and software engineering teams.

Responsibilities:

• 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
• 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
• 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
• 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
• 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
• 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

Requirements:

• Must work on shore in the US
• We cannot sponsor or transfer an H1B Visa for this 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
• Snowflake, Databricks, BigQuery, Redshift, or similar
• Apache Spark, Airflow, dbt, Kafka, streaming architectures
• AWS/Azure/GCP data services and infrastructure as code
• Dimensional modeling, data vault, data mesh principles
• SQL, Python, Scala, and data-specific programming paradigms
• Cloud data cost optimization, budget ownership, and ROI analysis
• Defining and tracking data platform KPIs and usage metrics
• Articulating data investments in business terms
• Capacity planning for data workloads and storage
• Evaluating and managing data tools and platform services
• Performance management, career development, and difficult conversations
• Hiring, onboarding, and creating inclusive team environments
• Technical and non-technical stakeholder management
• Data-driven decisions balancing multiple constraints
• Aligning data platform efforts with organizational goals
• Leading teams through platform migrations and tool adoptions
• Complex data and organizational challenge resolution
• Working effectively with Analytics, Data Science, and Engineering functions
• Developing junior and senior data engineers
• Identifying and implementing efficiency improvements
• Understanding business context and impact of data platform decisions