Senior Data Engineer

• Build and scale production-grade data pipelines for ingestion, transformation, validation, and delivery across batch and near real-time workflows.
• Design and maintain data platform architecture across Snowflake, Databricks, and cloud environments (AWS/GCP).
• Develop and optimise data models and high-performance SQL for analytics and AI workloads.
• Enable AI/ML use cases by structuring data for classification, forecasting, and embedding workflows.
• Implement data quality, validation, and monitoring frameworks to ensure reliability, accuracy, and observability.
• Drive performance optimisation and cost governance across distributed compute and cloud infrastructure.
• Build and maintain robust orchestration and CI/CD workflows, ensuring scalable, testable, and reliable data systems.

Requirements 

• 5+ years of experience as a Data Engineer, building and scaling production-grade data pipelines in cloud environments.
• Proven experience working with distributed data systems (e.g. Databricks, Spark) and modern data platforms (e.g. Snowflake).
• Expert-level SQL and strong Python (Pandas, PySpark/Snowpark) for building, optimising, and maintaining data pipelines and transformations.
• Experience with orchestration tools (Airflow, Prefect, or Dagster) and CI/CD workflows (e.g. GitHub Actions).
• Hands-on experience with cloud platforms (AWS or GCP), Docker, and distributed compute environments.
• Familiarity with data validation, testing, and monitoring frameworks (e.g. Great Expectations, dbt tests).
• Strong understanding of data architecture, schema design, and dimensional modelling.
• Experience with performance optimisation, cost governance, and building reliable, observable data systems.
• Ability to collaborate effectively with cross-functional teams (Data Science, Product, Platform).
• Strong problem-solving skills and ownership mindset in production environments).
• English fluency.