Investec - Where Out of the Ordinary Happens



At Investec, we do things differently. We're a leading international bank and wealth manager built on a culture of curiosity, entrepreneurial spirit and human connection. Ideas are heard, ambition is celebrated, and impact is encouraged. As part of a team that challenges convention and strives for outperformance, you'll help shape bold solutions for clients who expect something more than the ordinary.



We are seeking a highly skilled Data Engineer to engineer and deliver domain-aligned data products within a centrally governed Microsoft Data Platforms. Working within established standards, CI/CD templates, and defined data contracts, the Data Engineer will modernize SQL-driven legacy workloads and re-engineer them into scalable Spark-based Lakehouse datasets and semantic models. The focus is on execution: extracting embedded business logic from on-prem SQL systems, implementing robust data pipelines, and delivering reliable, high-quality data products that support analytics, reporting, and AI use cases.




Roles and Responsibilities:



We are seeking a highly skilled Data Engineer to engineer and deliver domain-aligned data products within a centrally governed Microsoft Data Platform.



Working within established standards, CI/CD templates, and defined data contracts, the Data Engineer will modernize SQL-driven legacy workloads and re-engineer them into scalable Spark-based Lakehouse datasets and semantic models.



The focus is on execution: extracting embedded business logic from on-prem SQL systems, implementing robust data pipelines, and delivering reliable, high-quality data products that support analytics, reporting, and AI use cases.



Data products are treated as managed assets - version-controlled, tested, monitored, and continuously improved to meet defined quality and performance expectations.




Key Responsibilities:




SQL Modernization & Legacy Refactoring

• Maintain and enhance existing SQL Server workloads (stored procedures, views, ETL processes).


• Refactor SQL-based transformations into scalable Spark-based ELT pipelines in Fabric.


• Translate dimensional models from legacy Kimball-based marts into modern Lakehouse structures.


• Support phased migration and controlled decommissioning of legacy assets.

Lakehouse Engineering in Microsoft Fabric and Databricks(Unity Catalog)

• Build and maintain bronze, silver, and gold layers in Microsoft Fabric and Databricks.


• Develop ingestion and transformation pipelines using:




• Notebooks


• Spark (PySpark preferred)




• Implement incremental loading and change data capture patterns from on-prem SQL to Fabric or Databricks.


• Optimize Delta tables for performance and cost efficiency.


• Ensure datasets align with defined data contracts and platform standards.

Semantic Modeling & Analytics Enablement

• Design and optimize semantic models in Fabric for enterprise analytics.


• Engineer star schemas and dimensional models for high-performance Power BI reporting.


• Ensure validated KPIs and business rules are consistently implemented.


• Balance performance, usability, and maintainability in semantic design.

Data Product Ownership

• Engineer data products that are reliable, discoverable, and consumption ready.


• Apply testing and validation to ensure data quality and integrity.


• Monitor pipeline health and performance using platform-defined observability standards.


• Contribute to continuous improvement of data products based on usage and feedback.


• Collaborate closely with business stakeholders, analysts, and the central Data Platform team.

Hybrid Data Operations

• Implement secure and reliable data movement between on-prem SQL environments and Fabric/Azure DataFactory.


• Support parallel run states during migration phases.


• Validate output consistency between legacy and modernized pipelines.


• Contribute to documentation and structured decommissioning of legacy workloads.

Azure Infrastructure as Code & DevOps

• Understanding and hands-on experience with Azure Bicep templates for automating infrastructure and pipeline deployments.


• Experience with source control, preferably Git.


• Experience implementing CI/CD processes and infrastructure automation, including automated deployment pipelines using Infrastructure as Code (IaC).


• Knowledge of Azure services such as Stream Analytics, Logic Apps, Function Apps, and Event Hubs would be an added advantage.

AI SDLC



• Basic knowledge of using AI tools to accelerate the build and development process across the data lifecycle, e.g.
GitHub Copilot, Codex, Claude and Databricks Genie
.



• Using AI
skills and agents to automate development processes, such as
scaffolding project folders, creating user stories and acceptance criteria, and generating pull requests
.




Core Skills and Knowledge:

• 6+ years of hands-on experience in data engineering within SQL-heavy or enterprise data environments.


• Strong expertise in Microsoft SQL Server and advanced T-SQL development, including query optimization and performance tuning.


• Proven experience refactoring legacy SQL-based ETL workloads into scalable, modern ELT pipelines.
  Practical experience working with Microsoft Fabric and Lakehouse architecture principles.


• Hands-on experience with Spark (PySpark preferred) for distributed data transformation, Python notebooks.


• Solid understanding of dimensional modeling, including star schema and Kimball-based methodologies.


• Experience in Azure Data Factory, Databricks, MS Fabric, ADLS Gen2 , Databricks , Spark , Airflow, purview.


• Experience designing and optimizing semantic models for enterprise analytics platforms such as Power BI.


• Experience implementing incremental loading and change data capture (CDC) patterns in hybrid environments.


• Experience building production-grade data pipelines with testing, monitoring, and CI/CD integration.


• Strong understanding of data governance principles, data contracts, and structured engineering standards.


• Experience working in enterprise-scale or regulated environments with disciplined delivery practices.


• Knowledge of web analytics platforms such as Adobe Analytics, Google Analytics, and Firebase would be an added advantage.

Desirable Skills : Any financial services / banking / capital market experience




Preferred Qualificaions :



• Microsoft certifications (Azure Data Engineer or Fabric Data Engineer Associate).



• Experience in distributed data processing environments.



• Exposure to AI/ML data preparation workflows.



Experience in enterprise-scale or regulated environments.




As part of our collaborative & agile culture, our working week is 4 days in the office and one day remote. We believe that being together enables us to live our values and support our clients and communities in an extraordinary way.



Embedded in our culture is a sense of belonging and inclusion. At Investec we want everyone to find it easy to be themselves, and to feel they belong. It's a responsibility we all share and is integral to our purpose and values as an organisation. We believe that innovation thrives when everyone feels respected, included, and empowered to contribute.



We actively seek out diverse talent and foster an inclusive environment, encouraging applications from people of all backgrounds and experiences. Here, you'll find networks, benefits, and development opportunities designed to support your career journey, wherever it may lead.




If this role excites you but you don't meet every requirement, we'd still love to hear from you. Your unique perspective and experience could be exactly what we are looking for. Get in touch!




At Investec, we're deeply invested in our clients, our colleagues, and our communities. It's more than a mindset; it's how we show up every day.




Be part of something Out of the Ordinary.




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