Data Engineer

Job:

. 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.

Role:

. 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.

Requirements:

. 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.

To apply:

Please send your latest CV in MS Word format to [Confidential Information]

Appreciate the interest of all applicants however only shortlisted candidates will be notified.

EA Licence No: 20S0237