Senior Data Engineer

Accylerate is a company seeking a Senior Data Engineer with a strong background in data quality and analysis. The role involves designing and implementing data quality checks, monitoring processes, and collaborating with cross-functional teams to ensure the accuracy and reliability of data used in reporting and operational workflows.

Responsibilities:

• 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

Requirements:

• 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., GitHub Copilot, Claude) and hands-on experience applying 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, QuickSight, 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