Data Observability Specialist

Data Quality (DQ) Observability

• Familiarity with cloud platforms and containerization technologies (Data Bricks).
• Proficiency in scripting and automation using languages such as Python,
• Excellent problem-solving skills with the ability to analyse complex issues and provide efficient solutions.
• Good communication skills and the ability to collaborate effectively across teams.
• Understanding of Agile and DevOps and GIT principles and their application in observability and monitoring contexts.
• Solid experience with instrumentation practices, including metrics, logging, and distributed tracing.
• E2E implementation. Right from requirement gathering and DQ design engaging with the Data Asset owners, development, and reporting.
• Development DQ rules with the help of Databricks
• Presentation of DQ results to the Data Asset Owners and other partners and work with them to build an action plan to improve the DQ results.
• Putting in place specific Data Improvement Plans
• Scale up the DQ framework to all the data assets and more.
• Ensure systems run efficiently, monitoring system performance and handle support in demanding environment.
• Handle the configuration of existing toolset for data quality, KPI system and Reporting.

Proficiency in implementing and maintaining observability tools is a plus

Qualifications And Experience Required

Skills Required

Pyspark, Python, Sql Data Base, Azure Databricks, Devops, Big Data Testing, Data Analysis