ROLE OVERVIEW :

We are looking for a Data Platform Lead to own the design, build, and governance of our enterprise data platform. This is a role within the Data COE, responsible for driving platform maturity across a federated, multi-cloud environment. You will set engineering standards, eliminate data duplication and drift across domains, and enable reliable, governed data access - without creating centralisation bottlenecks.

KEY RESPONSIBILITIES :

- Design and evolve the enterprise data platform spanning Snowflake and Databricks across Azure and AWS

- Build and maintain production-grade ELT/ETL pipelines using Spark, dbt, Airflow, and Kafka

- Define and enforce platform standards for ingestion, transformation, storage, and access across federated domain teams

- Drive data quality, data contract adoption, and lineage visibility across the estate

- Own cloud storage architecture across S3 and ADLS; lead CI/CD practices for all platform assets

- Lead containerized workload deployment using Kubernetes for data platform services

- Coordinate with domain teams to align on standards; represent platform engineering in architecture forums

- Mentor mid-level engineers and conduct technical design reviews for platform-impacting changes

MUST-HAVE SKILLS :

- Snowflake - expert-level : data modelling, performance tuning, role-based access, zero-copy sharing, dynamic data masking

- Databricks - production experience : Delta Lake, Unity Catalog, cluster management, Databricks Workflows

- Python and SQL - strong proficiency; Scala advantageous

- Apache Spark - distributed data processing at scale

- Apache Kafka - real-time event streaming, pipeline design, topic management

- Apache Airflow - pipeline orchestration, DAG design, dependency management

- DBT - transformation layer modelling, testing, documentation

- Cloud storage - S3 and ADLS; familiarity with Parquet, Delta, and partitioning strategies

- Kubernetes - container orchestration for data platform services

- CI/CD - GitHub Actions or equivalent for data platform assets (pipelines, schemas, infra)

- 8+ years of data engineering experience, with 3+ years in a lead or staff IC capacity

GOOD-TO-HAVE SKILLS

- Data quality frameworks - Great Expectations or equivalent, embedded in pipeline execution

- Feature Store platforms - Feast, Tecton, or equivalent; ML data infrastructure design

- Data catalogue and lineage tooling - Amundsen, DataHub, or equivalent

- Data contracts as an engineering practice - schema registries, versioning, producer/consumer agreements

- Open table formats - Apache Iceberg; lakehouse architecture patterns

- Cross-cloud experience - Snowflake (Azure), Databricks, and BigQuery integrations

WHAT WE'RE LOOKING FOR

- Systems thinker - able to see how pipeline, storage, and governance decisions interact at scale

- Able to influence without authority across autonomous, federated domain teams

- Opinionated on standards and quality, but pragmatic in how they are adopted across an existing estate

- Strong communicator - able to translate platform complexity to both engineering peers and business stakeholders

- Ownership-driven, with a track record of improving platform reliability, cost efficiency, and developer experience