• 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.

• Lead end-to-end data migration activities, including planning, mapping, transformation, validation, and reconciliation. • Define and execute data migration strategies and methodologies for Skadina implementations. • Oversee functional, integration, system, regression, and User Acceptance Testing (UAT) activities. • Ensure data quality, integrity, and consistency throughout migration and deployment phases. • Develop and review test plans, test cases, test scripts, and quality metrics. • Coordinate defect management, issue resolution, and root cause analysis activities. • Collaborate with architects, business analysts, developers, and business stakeholders to ensure successful project delivery. • Support cutover planning, deployment activities, and post-go-live stabilization. • Monitor project quality standards and ensure compliance with established processes and best practices. • Provide technical leadership and mentorship to project teams during migration and testing phases. • Prepare and maintain technical documentation, migration plans, and testing reports.

Role Description Enable Data is seeking an experienced Azure Data Architect with strong hands-on engineering expertise to design, develop, optimize, and modernize enterprise-scale data platforms. The role combines solution architecture, technical leadership, and individual contributor responsibilities, requiring deep expertise in Azure data services, data engineering best practices, and large-scale data processing using PySpark, Python, SQL, and Azure Databricks. The ideal candidate will be responsible for analyzing existing data products and pipelines, identifying improvement opportunities, leading refactoring and redesign initiatives, and implementing scalable, reliable, and high-performance data solutions on Azure. Responsibilities - Architecture & Solution Design - Overall 15+ years of experience with 10+ years of experience in Data Engineering and Analytics solutions. - Design end-to-end data architectures on Microsoft Azure. - Define data ingestion, transformation, storage, governance, and consumption strategies. - Create scalable, secure, and cost-effective data solutions aligned with business objectives. - Establish architectural standards, design patterns, and best practices for data engineering teams. - Collaborate with business stakeholders, product owners, and technical teams to translate requirements into technical solutions. - Design, develop, and modernize enterprise data platforms on Azure. - Analyze, refactor, and redesign existing data pipelines and products while building new scalable data solutions. - Act as a hands-on individual contributor with technical leadership responsibilities, including mentoring junior developers and driving best practices. - Strong hands-on experience in PySpark, Python, and SQL. - Expertise in Azure Databricks and Azure data ecosystem. - Experience designing and implementing scalable data architectures. - Ability to analyze, optimize, refactor, and redesign existing data pipelines. - Develop and maintain ETL/ELT solutions and data products. - Performance tuning, troubleshooting, and data quality implementation. - Mentor and guide junior developers and conduct code reviews. - Collaborate with cross-functional teams to deliver end-to-end data solutions. Requirements - Azure Data Lake, Azure Data Factory, Azure Synapse. - Data warehousing, lakehouse architecture, and cloud migration. - CI/CD, Git, and Agile development practices. Role Type - Senior Individual Contributor with Technical Leadership responsibilities.

• Architecture & Solution Design • Overall 15+ years of experience with 10+ years of experience in Data Engineering and Analytics solutions. • Design end-to-end data architectures on Microsoft Azure. • Define data ingestion, transformation, storage, governance, and consumption strategies. • Create scalable, secure, and cost-effective data solutions aligned with business objectives. • Establish architectural standards, design patterns, and best practices for data engineering teams. • Collaborate with business stakeholders, product owners, and technical teams to translate requirements into technical solutions. • Design, develop, and modernize enterprise data platforms on Azure. Analyze, refactor, and redesign existing data pipelines and products while building new scalable data solutions. • Act as a hands-on individual contributor with technical leadership responsibilities, including mentoring junior developers and driving best practices. • Strong hands-on experience in PySpark, Python, and SQL • Expertise in Azure Databricks and Azure data ecosystem • Experience designing and implementing scalable data architectures • Ability to analyze, optimize, refactor, and redesign existing data pipelines • Develop and maintain ETL/ELT solutions and data products • Performance tuning, troubleshooting, and data quality implementation • Mentor and guide junior developers and conduct code reviews • Collaborate with cross-functional teams to deliver end-to-end data solutions