Data Quality Engineering Lead
Job Summary
Job Description
This Data Quality Engineering Lead role requires developing and implementing data quality strategies, frameworks, and best practices for data warehousing projects. Responsibilities include establishing and enforcing data quality standards, leading a team of data quality engineers, designing and executing data validation processes, collaborating with cross-functional teams, investigating and resolving data quality issues, and staying updated on industry trends. The ideal candidate will have 10+ years of experience in data quality management, with at least 3 years in a leadership role, proficiency with data quality tools (e.g., Informatica, Talend, Collibra), strong SQL skills, and experience with BI tools and scripting languages. They will also possess excellent analytical, problem-solving, and communication skills.
Must have:
- 10+ years Data Quality experience (3+ years leadership)
- Data warehousing & ETL expertise
- Proficiency in data profiling, cleansing, validation
- SQL, database systems (Oracle, Snowflake, Redshift)
- Data quality tools (Informatica, Talend, Collibra)
- BI tools (Tableau, Power BI), scripting (Python, R)
- Excellent communication & problem-solving skills
- Lead & mentor data quality engineering team
Good to have:
- Big data platforms and cloud technologies (AWS, Azure, GCP)
- Agile methodologies and project management tools
14 skills required
Job Details
Description
Job Title: Data Quality Engineering Lead
Job Summary:
We are seeking a highly skilled and detail-oriented Data Quality Engineering Lead to ensure the accuracy, consistency, and reliability of data in our Enterprise Data warehousing engagements. The ideal candidate will lead the design and implementation of data quality frameworks, Data validation, enforce governance standards, and collaborate with cross-functional teams to deliver high-quality data solutions.
Key Responsibilities:
- Data Quality Strategy and Governance: Develop and implement data quality strategies, frameworks, and best practices for data warehousing projects. Establish and enforce data quality standards, policies, and procedures. Define metrics and KPIs to measure data quality and monitor compliance.
- Leadership and Team Management: Lead and mentor a team of data quality engineers, QA Engineers and analysts. Assign tasks, monitor progress, and ensure timely delivery of data quality objectives. Foster a culture of accountability, innovation, and continuous improvement within the team.
- Data Validation and Testing: Design and execute data validation, reconciliation, and testing processes to identify and resolve data anomalies. Develop automated data quality checks and tools to enhance efficiency and accuracy. Collaborate with data engineering teams to integrate quality checks into ETL workflows.
- Collaboration and Stakeholder Engagement: Work closely with data engineers, architects, and business stakeholders to understand data requirements and ensure alignment with business goals. Partner with business analysts and product teams to define data quality requirements and success criteria. Communicate data quality findings and recommendations to stakeholders in a clear and actionable manner.
- Issue Management and Resolution: Investigate and troubleshoot data quality issues, identify root causes, and implement corrective actions. Maintain a data quality issue log and track resolutions to closure.
- Continuous Improvement: Stay updated on industry trends, tools, and technologies in data quality management. Evaluate and recommend new tools and techniques to improve data quality processes. Drive the adoption of innovative data quality solutions across the organization.
Qualifications:
Education:
- Bachelor’s degree in Computer Science, Information Systems, or a related field (Master’s preferred).
Experience:
- 10+ years of experience in data quality management, with at least 3 years in a leadership role.
- Proven experience in datawarehousing and ETL processes.
- Expertise in data profiling, data cleansing, and data validation techniques.
Technical Skills:
- Proficiency with data quality tools (e.g., Informatica Data Quality, Talend Data Quality, Collibra).
- Strong knowledge of SQL and database systems (e.g., Oracle, Snowflake, Redshift).
- Experience with BI tools (e.g., Tableau, Power BI) and scripting languages (e.g., Python, R).
- Familiarity with big data platforms and cloud technologies (e.g., AWS, Azure, GCP).
- Experience with Agile methodologies and project management tools.
Soft Skills:
- Excellent analytical, problem-solving, and communication skills.
- Strong leadership and team management abilities.
- Ability to work in a fast-paced environment and manage multiple priorities effectively.
Work Environment:
- Remote/ Travel to client place for any meeting/ workshops.
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