Data Analyst – Data Quality & Process Assurance
Description
About the role
We are looking for a detail-oriented Data Analyst to support our Data Engineering function and help ensure that data is accurate, complete and processed correctly across our platforms. Working closely with the Data Engineer, business stakeholders and operational teams, you will validate data pipelines, investigate discrepancies, monitor data quality and confirm that agreed business rules are being applied throughout ingestion, transformation and reporting processes. This role is well suited to someone with strong SQL, analytical and problem-solving skills who enjoys understanding how data moves through systems and identifying where quality or process issues occur.
Key responsibilities
Support the Data Engineer in validating batch, microbatch and streaming data pipelines.
Perform data reconciliation between source systems, Kafka events, databases, object storage and downstream datasets.
Verify that data transformations, mappings and business rules are implemented correctly.
Develop and maintain data-quality checks covering completeness, accuracy, validity, consistency, uniqueness, timeliness and referential integrity.
Investigate missing, duplicated, delayed, invalid or incorrectly transformed records.
Analyse data issues to determine whether they originate from the source, ingestion process, transformation logic or downstream consumption.
Create SQL queries and validation scripts to compare source and target datasets.
Support testing of ETL, ELT, change data capture and event-processing workflows.
Monitor pipeline outcomes, record counts, processing status and data-quality metrics.
Document data definitions, mappings, transformation rules, lineage and validation procedures.
About you
Experience as a Data Analyst, Data Quality Analyst, Reporting Analyst or similar role.
Advanced SQL skills, including joins, aggregations, common table expressions, window functions and data reconciliation.
Strong analytical and investigative skills.
Experience validating data across source and target systems.
Understanding of ETL, ELT and data-transformation processes.
Experience defining and executing data-quality rules.
Ability to interpret business requirements and convert them into validation criteria.
Experience identifying trends, anomalies and exceptions in large datasets.
Strong attention to detail and a methodical approach to problem solving.
Effective communication skills and the ability to work with both technical and non-technical stakeholders.