Remote: Senior Data Engineer specializing in data quality governance to complete quality assessments, root cause analysis and optimize complex SQL queries

Our client is seeking a Senior Data Engineer specializing in data quality governance to complete quality assessments, root cause analysis and optimize complex SQL queries in a Snowflake environment.

Remote role open across Canada but require require MST working hours

Must Haves:

• 7+ years of experience writing and optimizing complex SQL queries for data profiling, analysis and to create data quality checks
• Skilled in interpreting data quality metrics, identify issues, and provide actionable insights and possible solutions
• 4+ years completing data quality assessments and root-cause analysis in Snowflake
• Experience with data governance principles and ability to enforce data quality standards and policies.
• Experience with Python for data manipulation, automation, and advanced data quality tasks.

Nice to Haves:

• Ability to debug and troubleshoot data quality issues and code-related problems effectively.
• Familiarity with DQ tools such as Montecarlo, DQ Labs, Ataccama, Owl DQ, IBM Information Analyzer, Informatica Data Quality etc

Key Responsibilities:

• 1. Data Quality Assessment and Monitoring:
• Perform comprehensive data quality assessments across various systems to evaluate accuracy, completeness, consistency, and reliability.
• Set up and manage ongoing data quality monitoring processes and tools.
• Develop and implement data quality metrics and frameworks to track and improve data quality over time.
• 2. Data Profiling and Requirement Analysis:
• Profile data to gain insights and identify trends to work with data teams in identifying the data quality checks.
• Identify data quality issues such as inconsistencies, inaccuracies, and missing values.
• Validate data integrity through automated and manual checks, ensuring data meets quality standards.
• 3. Data Quality Checks and Data Analysis:
• Write, optimize, and execute complex SQL queries to create data quality checks and measure the data from multiple sources.
• Use SQL for in-depth data analysis to identify and troubleshoot data quality issues.
• Configure and schedule Data Quality checks on the DQ framework to run as per the required schedules.
• 4. Root Cause Analysis and Remediation:
• Conduct root cause analysis to identify underlying issues contributing to data quality problems.
• Develop and implement remediation plans to address identified issues and prevent recurrence.
• 5. Documentation and Compliance:
• Document data quality issues, remediation actions, and processes clearly.
• Ensure adherence to data governance policies and standards, maintaining compliance with relevant regulations.
• 6. Stakeholder Engagement and Communication:
• Collaborate with data owners, IT, and business units to understand data quality requirements and address issues.
• Provide training and support to teams on data quality best practices and governance policies.
• Communicate data quality status and improvement efforts effectively to stakeholders.
• Oversee and manage data integration processes to ensure consistent data quality across systems.
• Ensure accurate and consistent data transformation to meet business needs.
• 7. Reporting and Dashboarding:
• Design, create, and maintain dashboards and reports to visualize data quality metrics and trends.
• Communicate findings, insights, and remediation progress to both technical and non-technical stakeholders through clear and actionable reports.