Analytics Engineering Manager, EvolutionIQ

• Experience: 5+ years of experience in analytics engineering, data engineering, or a highly technical data analytics role, with 1-2+ years of experience directly managing or leading engineers (e.g., Tech Lead or Team Lead)
• SQL Mastery: Expert-level SQL skills with the ability to write, optimize, and debug complex queries across large datasets
• Data Modeling Expert: Deep understanding of data warehousing concepts, ETL/ELT pipelines, and modeling methodologies (e.g., Kimball, Data Vault)
• Modern Data Stack: Hands-on experience with production-grade data warehouses (e.g., Snowflake, BigQuery, Databricks) and transformation tools like dbt (data build tool)
• Programming: Proficiency in Python or a similar language for writing custom data scripts, data tests, or orchestration tasks
• Software Mindset: Strong familiarity with Git workflows, CI/CD pipelines, and data quality testing frameworks
• Communication: Exceptional communication skills with a proven track record of bridging the gap between highly technical engineering teams and business-facing stakeholders

Desirable

• The Mission: We are looking for an Analytics Engineering Manager (Tech Lead Manager) to lead and scale our growing Analytics Engineering team. In this role, you will act as a "player/coach"—combining deep technical expertise in data modeling and architecture with a passion for mentoring and managing people
• As part of our centralized Data Organization, your team will sit at the intersection of Data Engineering, Data Science, and Business Analytics
• You will be responsible for building the robust, scalable data foundation that empowers our analysts to uncover insights and our data scientists to build predictive models, while directly managing the engineers making it happen
• Technical Leadership & Execution (60% "Player"):
• Data Architecture & Modeling: Lead the design, development, and maintenance of our centralized data warehouse layers using robust dimensional modeling practices
• Tooling & Infrastructure: Own and optimize our modern data stack (MDS), ensuring high performance, cost-efficiency, and reliability across our ingestion, transformation, and orchestration pipelines
• Code Quality & Best Practices: Set the standard for software engineering practices within data—including version control (Git), CI/CD, data testing, observability, and comprehensive documentation
• Cross-Functional Collaboration: Partner closely with Data Science and Business Analytics to understand their data needs, translating complex business logic into clean, reusable data models
• People Management & Mentorship (40% "Coach"):
• Team Leadership: Manage, mentor, and support a small, high-performing team of analytics engineers, fostering a culture of continuous learning, psychological safety, and technical excellence
• Career Development: Conduct regular 1-on-1s, define clear performance goals, provide actionable feedback, and guide team members through their career growth paths
• Resource Allocation: Manage the team’s sprint planning, prioritization, and roadmap, balancing technical debt with high-impact business requests
• Hiring & Scaling: Actively participate in recruiting and onboarding as we scale