Creating large, complex data sets that meet both functional and non-functional business requirements
Designing, developing, and maintaining scalable ETL pipelines and data workflows
Building reliable infrastructure to extract, transform, and load data from diverse sources -- often Cloud-based
Identifying opportunities to automate, optimise, and improve internal data processes
Developing tools and solutions that turn data into actionable business insights
Ensuring data quality, consistency, integrity, and security across environments
Driving best practices in data engineering, performance, and documentation
Skills & Experience You Bring
Bachelor's Degree in Computer Science, Engineering, Mathematics, or similar
5-7 years' experience in data engineering, database management, or a related role
Strong programming capability in Python or Scala
Advanced SQL skills and experience with relational databases (MSSQL, MySQL, PostgreSQL, etc.)
Exposure to NoSQL technologies
Proven experience in performance tuning and database optimisation
Solid understanding of data integration concepts and practices
Exposure to BI tools (Power BI, Yellowfin, etc.)
Experience with MS SQL Replication and data archiving strategies is beneficial
Experience with cloud technologies (AWS, Azure, GCP) and tools like S3, Lambda, Redshift, BigQuery, or Snowflake
Familiarity with big-data ecosystems such as Apache Spark, Databricks, or Hive
Understanding of data modelling, warehousing concepts, and data governance
Exposure to data cleansing and de-duplication strategies
Bonus Points For
Experience with streaming platforms (Kafka, Spark Streaming, Flink)
Knowledge of Docker and Kubernetes
Understanding of CI/CD and infrastructure-as-code
Exposure to machine learning workflows or MLOps
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