Data Warehouse Engineer

About our client:
Our client offers financial service solutions helping their clients achieve their dreams. With an emphasis on culture fit, they boast a dedicated team of over 600 employees, many with over a decade of tenure. They have built their culture on a feeling of togetherness, trust and respect and are always looking to support employees' continuous learning. Using Agile, they provide diverse services with a focus on research, innovation and improvement.
What you will be doing:

Design, implement, and maintain scalable data warehouse architectures (Star/Snowflake) and optimize ETL/ELT pipelines for data quality.

Write and tune complex SQL (T-SQL/ANSI) and use Python for data manipulation and advanced analytics.

Own the full SDLC: requirements, design, testing, deployment, and solution estimation.

Collaborate with stakeholders to translate business data needs into technical structures.

Identify and resolve performance bottlenecks through normalisation, indexing, and query optimization.

Develop and maintain dashboards/reports using tools like Power BI or Yellowfin for data-led decision-making.

Create and maintain high-quality technical documentation (data flow diagrams, specifications, metadata).

Actively participate in the Agile/Scrum framework and sprint ceremonies.

Research and evaluate emerging data technologies to continuously improve the data warehouse.

What our client is looking for:

A relevant tertiary qualification would be beneficial (Computer Science, IT, Data Science, etc.)

Strong knowledge of analytical & dimensional data warehouse modelling, design, architecture, data structures, and fundamental design principles.

Experience in developing and modifying ETL processes (Extract, Transform, Load) using various data sources, and understanding concepts like data normalisation and performance tuning.

Proficiency in relational databases (SQL Server, Postgre

SQL), SQL (TSQL/ANSI), and programming languages like Python.

Capability in the full Software Development Life Cycle, including solution estimation, requirements gathering, analysis (modifying data structures to meet client needs), technical design, unit testing, debugging, and documentation.

Ability to consult with clients to gather requirements, analyse business needs, propose technical alternatives, and provide necessary documentation (, technical specifications, data flow diagrams).

Understanding of Big Data Visualization Tools and specific industry BI Visualisation tools (, Yellowfin or Power BI).

Essential skills include communication (verbal & written, internal & external), problem-solving, and being a team player.