Senior Data Engineer

Data Quality Engineering & Monitoring

• Design and implement automated data quality checks for completeness, accuracy, consistency, freshness, and schema integrity across critical datasets and pipelines.
• Build monitoring, alerting, and observability solutions to detect anomalies, pipeline failures, data drift, and unexpected changes before they impact downstream consumers.
• Develop and maintain reconciliation processes across source systems, transformed datasets, reports, and operational outputs.
• Partner with engineers and analysts to define quality rules, acceptance criteria, and data validation requirements for new and existing systems.
• Create reusable frameworks, scripts, and tooling for profiling, testing, and validating data in production and non-production environments.

Investigation, Analysis, & Remediation

• Investigate data issues by tracing data across systems, transformations, and business workflows to identify root causes and recommend fixes.
• Use SQL, Python, and cloud data tools to analyze large datasets, isolate anomalies, and validate business logic.
• Support incident response and issue resolution for data-related production problems, especially during high-priority operational periods.
• Work with cross-functional teams to remediate defects, improve upstream processes, and reduce recurrence of common data issues.
• Communicate findings clearly to both technical and non-technical stakeholders, including issue summaries, remediation recommendations, and quality trends.

Governance, Documentation, & Team Success

• Document data definitions, validation logic, lineage, quality rules, and remediation procedures to improve transparency and operational readiness.
• Contribute to best practices for testing, version control, deployment, and ongoing maintenance of data quality solutions.
• Participate in Agile ceremonies, code reviews, and team planning, helping break work into manageable tasks and improve team productivity.
• Support the development of standards for data governance, ownership, and operational excellence across the team.
• Persistently partner with stakeholders to improve trust in shared data assets and ensure quality considerations are built into delivery from the start.