Big Data Engineer Analytics Track

Job Description: Big Data Engineer – Analytics Track

We are looking for an experienced Big Data Engineer – Analytics Track to build and maintain enterprise-scale analytics data products and reporting solutions. The role will focus on developing scalable data transformation pipelines using DBT, designing dimensional data models, enabling self-service analytics, and partnering with business stakeholders across Supply Chain, Finance, Sales, Customer, and Operations.

The ideal candidate will bring strong expertise in SQL, DBT, data warehousing, performance optimisation, and analytics engineering practices, with a focus on delivering reliable, production-grade data assets.

Technical Skills

• Strong hands-on experience with DBT (Data Build Tool)
• Expert-level SQL development
• Experience with Spark SQL, Trino, or Presto
• Strong understanding of data warehousing concepts
• Experience with dimensional modelling, including Star and Snowflake schemas
• Airflow workflow orchestration
• Git/Bitbucket version control
• Big Data Query performance tuning and optimisation
• Big Data architecture and implementation
• Data quality and reconciliation frameworks
• Database Technologies
• Oracle
• Hive
• Spark
• BI & Visualisation
• Metabase
• Tableau
• Power BI or equivalent BI platforms
• Key Responsibilities

Analytics Engineering & Data Transformation

• Design, develop, and maintain scalable DBT models and data pipelines.
• Apply Medallion and Data Mesh architecture principles.
• Build incremental and full-load transformation frameworks using DBT.
• Develop reusable macros, tests, snapshots, and documentation.
• Implement automated validation and reconciliation checks to ensure data quality.
• Optimise DBT models for performance, scalability, and cost efficiency.
• Data Modelling
• Develop enterprise and dimensional data models, including Star Schemas and Fact-Dimension structures.
• Design subject-area data marts for Finance, SCM, Customer, Sales, and Operations.
• Define KPI calculations, business metrics, and semantic-layer standards.
• Translate business requirements into well-designed analytical data products.
• Reporting & Analytics Enablement
• Support BI and reporting platforms such as Metabase, Power BI, Tableau, or equivalent tools.
• Build curated datasets for dashboards and self-service analytics.
• Develop reconciliation and operational monitoring reports.
• Enable conversational analytics and KPI-driven reporting frameworks.
• Engineering & DevOps
• Manage workflow orchestration and scheduling using Airflow.
• Implement CI/CD practices for DBT deployments.
• Conduct code reviews and establish engineering standards.
• Monitor production jobs and troubleshoot performance bottlenecks.
• Participate in release planning, deployments, and production support.
• Stakeholder Management
• Partner with business users and functional teams to understand requirements.
• Lead requirement workshops and solution discussions.
• Prepare technical documentation, HLDs, source-to-target mappings, and data lineage artefacts.
• Mentor junior developers and analysts.