Director, Data & Analytics Engineering

Role Overview

• Define and own the multi-year roadmap for the data platform, aligning investments in infrastructure, tooling, and headcount with business strategy.
• Lead and grow two high-performing teams—Data Engineering and Analytics Engineering—cultivating a collaborative, feedback-rich environment with clear career pathways.
• Architect and oversee scalable data pipelines across ingestion, transformation, orchestration, and delivery, for both batch and streaming use cases.
• Champion best practices in analytics engineering, including semantic layer design, dbt modelling standards, data contracts, and metrics governance.
• Partner with Data & Decision Science, Product, Finance, and Commercial teams to deliver high-quality, self-serve data solutions aligned to business needs.
• Ensure data platform reliability, observability, SLAs, and incident response, treating the platform as a product with real users.
• Drive vendor and tool evaluations for the modern data stack (cloud warehouse, orchestration, cataloging, transformation, reverse ETL, etc.).
• Set and enforce data quality, documentation, and governance standards to build trust across the business.
• Champion use of AI coding assistants and LLM-powered tooling (e.g. Cursor, GitHub Copilot, Claude) to accelerate delivery and reduce toil.
• Implement AI-native patterns—LLM-generated documentation, anomaly detection, data quality monitoring, and automated root-cause analysis.
• Prototype NL-to-SQL and AI-powered BI tools to empower self-serve analytics for non-technical users.
• Build foundational data infrastructure (feature stores, vector stores, model metadata, evaluation datasets) to enable AI and ML experimentation and scale.

Requirements

• 7+ years in data engineering or analytics engineering, with 3+ years in a senior leadership role managing multiple teams
• Deep expertise in the modern data stack—cloud data warehouses (Snowflake, BigQuery, or Databricks), dbt, orchestration tools (Airflow, Dagster, or Prefect), and ELT frameworks
• Proven ability to define and execute a multi-year data platform strategy
• Strong stakeholder management, including executive presentations and translating technical concepts to non-technical audiences
• Experience building and scaling high-performing engineering teams: hiring, mentoring, performance management
• Track record of delivering trusted, well-documented, and widely adopted data products.
• Hands-on experience integrating AI/LLM tooling into engineering workflows or data products (it would be great if you also had)
• Familiarity with semantic layer tools (e.g. MetricFlow, Cube), data cataloging (e.g. Atlan, Datahub), and data observability platforms (it would be great if you also had)
• Experience with streaming data (Kafka, Flink, or Kinesis) and batch processing (it would be great if you also had)
• Knowledge of ML infrastructure: feature stores, model serving, vector databases (it would be great if you also had)
• Exposure to data mesh or data product organizational models (it would be great if you also had)
• Strong command of SQL and Python

Tech Stack

• Airflow
• BigQuery
• Cloud
• ETL
• Kafka
• Python
• SQL

Benefits

• Competitive compensation, plus all full-time employees participate in our ownership program
• because everyone should have a stake in our success.
• Flexible work culture. Our remote, hybrid and in-office collaboration spaces vary by role, team and location.
• Generous time off, including local holidays and our annual “Dim the Lights” period in late December, when teams are encouraged to step back and recharge based on departmental needs.
• Comprehensive wellness programs and mental health support
• Annual learning and development stipends to support your growth
• The technology and tools you need to do your best work
• Motivosity employee recognition program
• A culture rooted in inclusivity, support, and meaningful connection