Data Quality Analyst Job at Match Point Solutions, Maryland Heights, MO

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  • Match Point Solutions
  • Maryland Heights, MO

Job Description

MatchPoint Solutions is a fast-growing, young, energetic global IT-Engineering services company with clients across the US. We provide technology solutions to various clients like Uber, Robinhood, Netflix, Airbnb, Google, Sephora, and more! More recently, we have expanded to working internationally in Canada, China, Ireland, UK, Brazil, and India. Through our culture of innovation, we inspire, build, and deliver business results, from idea to outcome. We keep our clients on the cutting edge of the latest technologies and provide solutions by using industry-specific best practices and expertise.

We are excited to be continuously expanding our team. If you are interested in this position, please send over your updated resume. We look forward to hearing from you!

Title: Data Quality Analyst

Location: Remote, preferred by Oakland, CA Rancho Cordova, CA or Alpharetta, GA.

Duration: 6 months+

Rate : $55 /hr w2

Position Overview:

We are seeking a highly experienced Data Quality (DQ) Specialist to lead and drive our organization's data quality initiatives end-to-end. The successful candidate will review existing Data Quality frameworks, perform detailed data profiling, collaborate with stakeholders to identify critical data elements and define DQ rules, and ensure seamless implementation in partnership with the Technology team. The role requires an independent, proactive leader with strong communication, problem-solving, and program management skills, capable of coordinating cross-functional teams to monitor, communicate, and resolve data issues effectively.

Key Responsibilities:

  • Review the current Data Quality framework, evaluate its effectiveness, and provide actionable feedback for improvements.
  • Conduct comprehensive data profiling using SQL or specialized tools, analyze results, and summarize key issues and recommended actions.
  • Collaborate with business stakeholders to identify Critical Data Elements (CDEs) and define clear Data Quality rules aligned with organizational standards.
  • Work closely with the Technology team to ensure Data Quality rules are properly implemented and automated within data systems.
  • Establish a robust process for ongoing monitoring, issue detection, and issue resolution management, including communication channels for stakeholders.
  • Drive data quality discussions and initiatives independently, fostering alignment and accountability across cross-functional teams.
  • Develop and maintain documentation on DQ processes, issues, and resolutions; create dashboards and reports for ongoing oversight.
  • Lead root-cause analysis and problem-solving efforts to address data quality issues, ensuring timely resolution and continuous improvement.
  • Communicate findings, issues, and resolutions effectively through verbal presentations, detailed reports, and stakeholder meetings.
  • Promote a product mindset, focusing on delivering high-quality, reliable data as a strategic organizational asset.
  • Manage end-to-end DQ projects within program timelines, aligning activities with broader data governance and organizational objectives. Qualifications:
  • Proven experience in Data Quality frameworks, profiling, issue resolution, and monitoring.
  • Strong SQL skills; experience with data profiling tools or platforms is a plus.
  • Knowledge of Data Governance principles and best practices.
  • Excellent problem-solving and root-cause analysis skills.
  • Effective verbal, written, and presentation communication skills.
  • Demonstrated ability to work independently and lead cross-functional discussions.
  • Strong project management and program delivery experience, with a focus on data-centric initiatives.
  • Familiarity with data systems, architectures, and tools used within large enterprise environments. Preferred:
  • Experience in the healthcare, finance, or data-intensive sectors.
  • Familiarity with data product management or end-to-end data delivery frameworks.

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