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Metropolis Technologies is seeking a Director of Data Engineering & Analytics to lead and scale the company's data organization. You will oversee three high-performing teams—Data Engineering, Analytics Engineering, and BI Development—and own the end-to-end strategy, architecture, and execution of a modern, scalable data platform that serves as a critical foundation for AI-driven analytics, decision-making, and business intelligence across the organization.
Key responsibilities include leading and mentoring multi-team data organizations while establishing engineering best practices, career development frameworks, and performance standards. You will define team charters, OKRs, and strategic roadmaps aligned with company goals, and drive hiring and retention of top engineering talent. You'll own the complete data platform vision from ingestion and storage through transformation, serving, and consumption layers, driving adoption of modern data stack technologies including dbt, Spark, Airflow, Snowflake, BigQuery, Databricks, and Kafka.
You will ensure platform governance, data quality, lineage, observability, and compliance standards in partnership with Security teams. A key focus is championing AI/ML infrastructure by partnering with Data Science and AI Engineering teams to power real-time and batch insights, accelerate model development, and prepare data for generative AI and traditional ML workflows. You'll also oversee analytics engineering and BI development to deliver reliable semantic models, self-serve dashboards, embedded analytics, and foster a data-as-a-product mindset.
As a strategic partner to executive leadership, you will translate business needs into data platform investments and collaborate across Engineering, Product, Finance, and Operations.
Required qualifications: 8+ years in data engineering, analytics engineering, or data infrastructure; 3+ years managing multi-team or multi-functional data organizations; deep expertise in modern data stack tools, ELT pipelines, cloud data warehouses, dbt, orchestration frameworks, and data modeling; proven track record building and scaling data platforms supporting AI/ML workloads including feature stores and ML pipeline infrastructure; strong experience with Snowflake, BigQuery, or Databricks; demonstrated ability to recruit, develop, and retain high-performing engineers; excellent communication skills translating complex technical concepts for business audiences.
Desirable experience includes LLM integration and generative AI infrastructure (RAG pipelines, embedding pipelines, prompt evaluation), data mesh or data product frameworks, fast-growth startup or scale-up environments, and backgrounds in analytics, business intelligence, or data science alongside engineering.
Metropolis values in-person collaboration and operates an office-first model requiring on-site presence at least four days per week.
