Data analytics test engineer (00012460)

Brief Description

(IT) Designing and implementing big data technologies through the application of high performance computing, large scale data integration and emerging analytical platforms.

Requirements

The Data Analytics Test Engineer is responsible for ensuring the quality, integrity, and reliability of data across the organisation's enterprise data platform. The role focuses on designing, developing, and executing robust testing strategies for data pipelines, data models, and analytical solutions. It also involves translating business needs into clear technical requirements, driving automation, strengthening data quality assurance practices, and enabling accurate, trusted insights that support informed business decision making.

Key Accountabilities

• Drive data requirements analysis by partnering with business and technical teams to translate data needs into actionable requirements.
• Champion data quality through profiling, completeness checks, reconciliation, and anomaly detection.
• Lead end-to-end testing and UAT including scenario planning, execution, defect management, and audit-ready sign‐off.
• Validate ETL/ELT pipelines across cloud platforms, ensuring accuracy of ingestion, transformations, and data flows.
• Build and enhance test automation to improve regression coverage and efficiency.
• Maintain high quality documentation including test cases, data dictionaries, mapping specifications, and validation rules.
• Continuously improve testing practices by enhancing frameworks, processes, and quality controls.
• Collaborate with cross-functional teams such as Data Engineers, Product Owners, BAs, and Reporting teams.
• Ensure governance and compliance by applying data privacy, security, and quality standards.

Technical Qualifications And Experience

• 5+ years' experience across data analysis, data validation, data quality, and testing within modern data platforms.
• Hands‐on experience with Databricks including Spark, Delta Lake, notebooks, workflows, and Lakehouse environments or equivalent modern data platforms.
• Proven ability to automate QA and testing for data pipelines and products.
• Strong ETL/ELT and data engineering exposure with understanding of ingestion patterns, transformation logic, and cloud architectures.
• Experience with Azure DevOps or similar CI/CD tools supporting automated testing and deployment.
• Demonstrated cross‐functional collaboration with engineering, product, and analytics teams.

Skills That Will Help You Succeed

• Proficient in PySpark, Python and SQL for data analysis, automation, and testing.
• Strong data profiling and validation capability with the ability to quickly identify data quality issues.
• Solid understanding of dimensional modelling including Star and Snowflake schemas.
• Good knowledge of Data Lake architecture across persistent, unified, and business access layers.
• Ability to interpret data requirements and solution designs and translate them into effective testing approaches.
• Strong planning and time‐management skills to manage competing priorities.
• Excellent written and verbal communication skills for engaging with technical and non‐technical stakeholders.