Data Quality Assurance / ETL Tester - Quezon City

apartmentAscendion placeQuezon City scheduleFull-time calendar_month 
Create detailed test cases that match business requirements to ensure data quality.
  • Perform data quality checking for ad-hoc data extraction.
  • Ability to test in a timely manner that meets SLA.
  • Communicate with Data analyst and Project Management officer to gather and analyze requirements for test case creation.
  • Support data analyst in peer reviewing data output before user’s acceptance (UAT).
  • Maintain QA documentation/artifacts for future testing purposes.
  • Perform data testing of outputs made by data analysts to ensure quality using SQL.
  • Ability to work cohesively in a team type environment.
  • Attend daily stand-ups for team updates and work progress.
  • Displays (The Knowledge, Skills, and Behaviors indicating how tasks / responsibilities will be performed)
  • Detail oriented to identify test cases/scenarios to test data output produced by data analysts.
  • Familiar with basic QA (quality assurance) terminologies and concepts.
  • Willingness to work with different stakeholders across the organization
  • Strong collaboration skills
  • Agile mindset with ‘fail fast, learn fast’ attitude
  • Experience in analytics and data warehousing concepts, terminologies, and architecture
  • Strong collaboration skills
  • Agile mindset with ‘fail fast, learn fast’ attitude
  • Experience in analytics and data warehousing concepts, terminologies, and architecture
  • Good oral and presentation skills are a must
  • Excellent team player with good analytical skills in providing insights to numbers
  • Ability to work closely with colleagues to try and identify data related problems and solve them
  • Delivers (The specific outputs / tangible results produced by the role; resources responsible for)
  • Test cases to test if data outputs made by data analyst’s meet business requirements.
  • Data quality checking to prevent data issues such as duplicate data, missing values etc.
  • Elevate data quality related KPI’s and reduce rework due to data issues and missed requirements.
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