Director, Data & Analytics Engineering
US-REMOTEFull-TimeDirector
Salary not disclosed
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Job Details
- Experience
- 7+ years in data engineering or analytics engineering, with 3+ years in a senior leadership role managing multiple teams
- Required Skills
- PythonSQLSnowflakeAirflowBigQuerydbtDatabricks
Requirements
- 7+ years in data engineering or analytics engineering
- 3+ years in a senior leadership role managing multiple teams
- Deep expertise in the modern data stack
- Experience with cloud data warehouses (Snowflake, BigQuery, or Databricks)
- Experience with dbt
- Experience with orchestration tools (Airflow, Dagster, or Prefect)
- Experience with ELT frameworks
- Proven ability to define and execute a multi-year data platform strategy
- Strong stakeholder management, including executive presentations
- Ability to translate 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 (would be great to have)
- Familiarity with semantic layer tools (e.g. MetricFlow, Cube) (would be great to have)
- Familiarity with data cataloging (e.g. Atlan, Datahub) (would be great to have)
- Familiarity with data observability platforms (would be great to have)
- Experience with streaming data (Kafka, Flink, or Kinesis) and batch processing (would be great to have)
- Knowledge of ML infrastructure: feature stores, model serving, vector databases (would be great to have)
- Exposure to data mesh or data product organizational models (would be great to have)
- Strong command of SQL and Python (would be great to have)
Responsibilities
- Own the architecture and roadmap for the modern data stack, from source systems through to consumption layers.
- Hire, grow, and inspire both data engineers and analytics engineers, fostering a culture of quality, curiosity, and ownership.
- Embed AI tooling natively into the team’s workflows for build, testing, documentation, and monitoring of our data platform.
- Translate commercial priorities into robust data infrastructure that is agile, trusted, and scalable.
- 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.
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