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

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringanalytics engineeringcloud data warehousesdbtAirflowDagsterPrefectSQLPythonstreaming data

Soft Skills

stakeholder managementexecutive presentationsmentoringperformance managementcollaborationfeedback-rich environmenttranslating technical conceptsbuilding trustteam leadershipcommunication