Senior Data Analyst - GCP, PostregSQL, AWS, ETL, SQL - Hybrid

Senior Data Analyst - GCP, PostregSQL, AWS, ETL, SQL - Hybrid, Milton Keynes

A large global organisation are looking to hire a Senior Data Analyst to lead end-to-end data workflows from requirement to delivery including data product creation and secure data transfer from Google Cloud Platform to PostgreSQL.

This will be a full time permanent position.

The role will be on a Hybrid/Remote basis in Milton Keynes, UK.

Key Responsibilities/Skills

• Develop & Schedule SQL Views via DAGs
• Design and implement SQL views aligned with business needs, prioritizing clarity, reusability, and efficiency.
• Build and manage workflow orchestrations (eg, Airflow DAGs) to automate those views, ensuring reliable execution on daily, weekly, or customized schedules.
• Execute Cross Platform ETL with AWS Glue
• Develop, deploy, and maintain AWS Glue jobs to extract data from GCP (such as BigQuery or GCS) and load it into PostgreSQL.
• Set up secure connectivity, schedule jobs via cron or trigger mechanisms, and ensure data pipelines are reliable and idempotent.
• Monitor, Troubleshoot & Resolve Incidents
• Continuously oversee ETL workflows in Airflow and AWS Glue, proactively responding to alerts and errors.
• Conduct root cause analysis for pipeline failures-whether due to schema mismatches or performance bottlenecks-and apply robust fixes. Document resolutions to strengthen system resilience.
• Design, Build, & Govern Data Products
• Architect, construct, and maintain reusable data products, embedding clean datasets, metadata, governance policies, and clearly defined data contracts.
• Ensure compliance with FAIR principles-data being Findable, Accessible, Interoperable, and Reusable-and enforce robust access controls in collaboration with governance stakeholders.
• Translate Requirements into Technical Designs
• Accumulate and analyze requirements via stakeholder engagement, user stories, or use cases.
• Convert these into detailed design artifacts, including architecture diagrams, data models, and specifications for development.
• Optimize Performance Across the Stack
• Continuously refine ETL pipelines, SQL logic, and data workflows to boost efficiency and scalability. Techniques may include indexing, partitioning, caching, or employing materialized views to improve query speed.
• Lead Migration from hh360 to BigQuery
• Architect and drive a seamless migration strategy to move data and pipelines from the Legacy hh360 system into Google BigQuery.
• Employ iterative migration patterns for safe data transfers, rigorous validation, and phased deprecation of Legacy infrastructure.