Data Engineer

• Own the design, development, and scaling of our data platform and pipelines.
• Lead a small team of data engineers, work closely with Data Science, Analytics, and Product, and ensure our data is reliable, scalable, and accessible for decision-making and ML products.
• Be hands‑on with architecture and coding, while also mentoring engineers and setting engineering standards.

• Design and implement scalable, reliable, and cost‑effective data pipelines and data platforms.
• Build and maintain ETL/ELT pipelines, data models, and APIs using Python, SQL, and modern cloud tools.
• Own data ingestion, storage, transformation, orchestration, and monitoring in cloud environments.
• Guide engineers, conduct code reviews, and drive best practices in data engineering.
• Partner with Data Science, Analytics, Product, and Business teams to translate requirements into technical solutions.
• Drive end‑to‑end delivery of data projects from requirements gathering to production deployment and support.
• Implement testing, monitoring, and data quality frameworks.
• Enforce data security, privacy, and compliance standards.
• Optimize pipeline performance, cost, and reliability.
• Troubleshoot production issues.

• Masters or Degree in Computer Science, Engineering, Mathematics, or related field.
• Equivalent practical experience will be considered.
• Has at least 6+ years in data engineering, with 2+ years in a lead/senior role.
• Strong in Python and SQL. Experience with PySpark is a plus.
• Hands‑on experience with AWS, GCP, or Azure. Services like S3/ADLS, BigQuery/Redshift/Snowflake, Airflow, Kafka, DBT.
• ETL/ELT Tools: Informatica PowerCenter / Informatica Intelligent Cloud Services (IICS).
• Experience with data integration, mappings, transformations, and workflow orchestration in Informatica.
• Strong experience with dimensional modeling, data warehousing, and lakehouse architectures.
• Experience with Airflow, Prefect, or similar workflow tools.
• Engineering practices: CI/CD, Docker, Git, Infrastructure as Code, unit testing.
• Good to Have: Kafka, Spark Streaming, Flink; Databases: PostgreSQL, MongoDB, Redis.
• Working with feature stores and model deployment pipelines.
• Experience hiring, mentoring, and doing technical interviews.
• Can explain complex technical concepts to non‑technical stakeholders.
• Proactive problem solver who takes end‑to‑end ownership.
• Works well across Data Science, Analytics, and Engineering teams.
• The role will need to secure a clearance, due to the sensitivity of the project.

EA Licence No: 20S0237