Senior Data Engineer

As a Senior Data Engineer you’ll help ensure that the data powering our products, reporting, and operational workflows is accurate, complete, timely, and trustworthy. You’ll design and implement data quality checks, monitoring, and remediation processes across pipelines and platforms, working closely with engineers, analysts, product teams, and business stakeholders. Your work will help identify issues early, reduce operational risk, and improve confidence in the data used to support critical decisions and digital assessment experiences.

This role blends hands‑on technical investigation, automation, and cross‑functional collaboration.

In this role you will:

• 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.

• 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.

• 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.
• Partner with stakeholders to improve trust in shared data assets and ensure quality considerations are built into delivery from the start.

• BS degree in Engineering, Computer Science, or related field / equivalent experience.
• 10+ years of general experience in quality testing.
• Strong SQL skills and experience writing complex queries to analyze, validate, and troubleshoot data across multiple systems.
• Professional experience in data engineering, analytics engineering, data quality, software engineering, or a related field with a strong focus on data investigation and validation.
• Exposure to AI‑assisted development tools (e.g., Git Hub Copilot, Claude) and hands‑on experience applying them to build and deploy AI agents that automate data pipelines, write code and testing workflows.
• Experience working with cloud data platforms and tools such as AWS, Redshift, Athena, Snowflake, Databricks, or similar technologies.
• Proficiency in Python or Type Script language used for automation, testing, and data analysis.
• Experience designing or maintaining data quality checks, monitoring, alerting, or observability processes for production datasets or pipelines.
• Strong understanding of data structures, data modeling, transformations, lineage, and common sources of data defects.
• Ability to investigate issues across systems, apply business logic, and translate ambiguous problems into structured analysis and action.
• Experience working with BI/reporting tools such as Tableau, Quick Sight, or similar platforms is helpful.
• Strong communication, documentation, and collaboration skills, with the ability to work effectively across technical and non‑technical teams.
• A learner’s mindset, curiosity about emerging technologies and AI‑enabled tools, and a drive to improve systems and processes continuously.
• Ability to support high‑priority operational periods and respond effectively to production data issues when needed.
• Strong interpersonal and consultative skills.
• Highly self‑motivated and directed, with keen attention to detail.
• Strong leadership skills and customer satisfaction orientation.