• Analyze raw data from various sources. • Develop and maintain datasets, schemas and models. • Improve data quality and efficiency. • Document data flows and mappings. • Collaborate with data analysts, scientists and other stakeholders. • Perform related duties as assigned. • Develop internal/external reports for information that is coming from ORR. • Assist with buildout of data dictionary through SharePoint. • Analyze data sets to ensure alignment to the overall organizational focus. • Advise on updated reporting to capture data through mapping and format it into a useable format which can be easily interpreted by other departments. • Bring innovation and creativity to legacy technologies and programs to drive/shift changes.

• Design and automate scalable data ingestion and transformation pipelines across relational, event-based, and unstructured sources. • Build and maintain frameworks to monitor, detect, and resolve data quality and integrity issues. Implement data governance practices, including metadata management, data access, and retention policies. • Architect and guide development of reliable, efficient, and scalable ETL/ELT data pipelines with monitoring and alerting. • Design physical data models and optimize database structures, indexing, and relationships for performance. • Test, optimize, and troubleshoot data pipelines to ensure stability and performance. • Develop and manage large-scale data storage solutions using distributed and cloud platforms (e.g., data lakes, Hadoop, NoSQL databases). • Drive automation and modernization of data infrastructure and integration processes to support agile analytics initiatives.

• Design and maintain ingestion & transformation flows (dbt, SQL, Python). • Build reliable pipelines from ETLs, CDC, APIs, CSVs, and event streams into analytical models. • Ensure deduplication and data integrity for BI reporting. • Develop semantic and analytical models (fact/dimension tables, star/snowflake schemas). • Optimize query performance in BI warehouse (currently Athena with Iceberg but in the future maybe ClickHouse, Redshift, BigQuery or Starrocks). • Implement versioned entities and audit trails for business data. • Build pre-aggregations and semantic layers (Cube, dbt metrics and models). • Support dashboards and client-facing reports in Grafana. • Design KPIs and metrics with product, risk, and operations teams. • Implement validation checks (dbt tests, Pydantic). • Monitor data freshness, pipeline SLAs, and dashboard accuracy. • Set up alerts and documentation for business users. • Work closely with analysts, product managers, and operations to understand BI needs. • Expose data via APIs/semantic layers for self-service exploration. • Contribute to the design of rules for reporting policies and metric definitions.

• Manage and optimise Snowflake data warehouses. • Overhaul Snowflake warehouse performance through materialisation strategies, dynamic tables, and clustering optimisation. • Build scalable ETL/ELT pipelines using dbt, Airflow, Fivetran, and AWS DMS. • Build ingestion pipelines using DMS and Fivetran. • Develop modelling layers in dbt using medallion architecture principles. • Transform raw data into clean, reliable, and business-ready models using dbt and AI-assisted tooling for documentation and testing. • Integrate data from multiple sources, including CRM systems, payment platforms, gaming platforms, and other operational systems. • Own initiatives focused on data quality improvements and monitoring, including anomaly detection and automated alerting. • Monitor and optimise platform performance, cost efficiency, and security. • Work closely with cross-functional teams across product development, data, operations, and analytics functions. • Collaborate with the data science team and support colleagues with reporting and analytics activities when required. • Support the organisation’s move towards real-time data ingestion and ETL using technologies such as DMS, Kafka, and Kinesis. • Help mentor other engineers within the team. • Contribute to the team's AI strategy and promote effective use of AI tools across engineering workflows. • Produce and maintain clear, comprehensive documentation to support scalability, transparency, and long-term platform sustainability. • Communicate effectively with both technical and non-technical stakeholders and proactively raise blockers when encountered.