Director, Data Engineering
New
United KingdomFull-TimeDirector
Salary not disclosed
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Job Details
- Experience
- 3+ years in data engineering with at least 2+ years in a senior or leadership role.
- Required Skills
- AWSPythonSQLAirflowSparkdbtDatabricks
Requirements
- 3+ years in data engineering with at least 2+ years in a senior or leadership role.
- Strong hands-on expertise with Databricks (Delta Lake, Unity Catalog, Spark) and modern data lakehouse architectures.
- Proficiency in Python and SQL for building scalable data pipelines.
- Experience with dbt or similar transformation frameworks.
- Strong background in building and operating data pipelines using tools such as Fivetran, Airflow, or equivalents.
- Deep understanding of data modelling, data warehousing, and distributed data systems.
- Proven ability to define and enforce engineering best practices (testing, CI/CD, observability, documentation).
- Experience working with cloud platforms, ideally AWS.
- Strong communication skills with the ability to translate technical decisions into business impact.
- Ability to balance hands-on engineering with leadership and cross-functional collaboration.
- Experience in complex environments such as M&A, multi-system integrations, or platform migrations is highly valuable.
- Familiarity with data quality, governance, BI tools, and modern analytics ecosystems is a plus.
Responsibilities
- Lead, mentor, and grow a team of data engineers, fostering a culture of ownership, collaboration, and technical excellence.
- Contribute hands-on to the design and development of data pipelines, integrations, and core platform components.
- Own and evolve a Databricks-based lakehouse architecture, ensuring scalability, performance, and maintainability.
- Define and enforce engineering standards across ingestion, transformation (dbt), testing, CI/CD, documentation, and observability.
- Design robust ingestion frameworks for complex, multi-source environments, including M&A-driven integrations.
- Ensure reliable data pipelines with strong monitoring, lineage, and full historical traceability.
- Partner with analytics and governance teams to ensure data models and contracts support downstream business use cases.
- Drive operational excellence, including incident response, reliability improvements, and SLA adherence.
- Support hiring, team scaling, and long-term engineering capacity planning.
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