Analytics Engineer
Role details
Job location
Tech stack
Job description
In this role, you will design, implement, and optimise inclusive, high-quality data models and platform performance to enable accurate, efficient analytics and support data-driven decision-making across the organisation.
You will collaborate with diverse stakeholders and empower users through technical support, training, and best-practice guidance, ensuring everyone can confidently access, understand, and maximise the value of the platform., * Design and implement data models and indexes required to support the platform and ensuring that the data is organized and structured in a way that enables efficient and effective analysis.
- Design and implement data models optimized for analytics ensuring high levels of data quality and integrity for accurate analytics outcomes.
- Provide technical support to end users, resolving any technical issues that arise, and ensuring that the platform is functioning effectively and efficiently.
- Migrate data from existing systems into the platform and ensuring that the data is structured and organized in a way that enables effective analysis.
- Monitor the performance of the platform, identifying areas for improvement, and implementing optimizations to ensure that the platform is functioning efficiently and effectively
- Providing training and support to end users, helping them to understand and use the platform effectively, and ensuring that they are able to extract the maximum value from the platform.
- Stay up to date with the latest developments in the data analytics and business intelligence industries, and ensuring that the platform is configured and optimized to meet the latest standards and best practices.
- Collaborate with cross functional stakeholder groups to align data outputs with dynamic business needs
Key Requirements
Semantic modelling + KPI governance
- Can design and maintain enterprise KPI definitions and semantic models that remain stable as requirements evolve.
- Has operated a modelling / release process, standards, reviews, and safe rollouts for analytics assets.
Lakehouse-first analytics delivery
- Comfortable building and supporting analytics assets where Databricks is the consumption plane, and data is structured through Raw/Cleansed/Curated layers on AWS S3.
- Works effectively within Unity Catalog governance and quality controls data contracts + monitoring via Soda.
BI platform engineering
- Power BI: proven capability to maintain semantic models at scale and implement pragmatic operational practices, model hygiene and repeatable maintenance.
- ThoughtSpot: can implement/assess RLS options and manage user/group access patterns.
- Demonstrated ability to improve platform reliability via proactive practices (alerts/processes) rather than reactive firefighting.
Engineering discipline for analytics
- Uses version control and follows controlled change/release practices for analytics assets; understands why analytics must be treated as production-grade.
Stakeholder delivery
- Can translate business questions into durable datasets and explain changes clearly to stakeholders.
Requirements
- Experience with dbt-like practices (modular models, tests, docs, CI)
- Familiarity with Microsoft guidance for Power BI lifecycle management and managing change across environments.
- Exposure to BDH ecosystem tools such as Dataiku and catalogue patterns
- Awareness of BDH ingestion patterns (e.g., Lakeflow Connect/Boomi) to anticipate downstream impacts. *, Adaptability, Data Architecture Development, Data Engineering, Data Integration, Data Integrity, Data Modeling, Data Security, Empathy, Experimentation, Microsoft Power Business Intelligence (BI), Python (Programming Language), Tableau (Software), Taking Ownership, Teamwork, Understand Customers