Senior Data Engineering Manager/Coach

• 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

• 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

• 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

• 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

• 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

• 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

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

Statement for Speridian Technology

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.