01Responsibilities
Lead the design, development, and implementation of enterprise-level data quality frameworks and solutions.Translate complex business requirements and data governance policies into executable data quality rules, checks, and processes.Implement real-time data quality solutions and data observability leveraging tools such as Monte Carlo, Dynatrace, or custom solutions.Provide subject matter expertise in data quality, data observability concepts, and their practical application.Develop custom data quality solutions using SQL, Python, or other relevant programming languages when off-the-shelf tools are not sufficient.Design and optimize data quality checks across various data platforms and technologies, including modern data stacks (Spark, Scala, PySpark, Databricks, GCP Data services). and traditional ETL tools (Informatica BDM).Ensure data quality for various data storage solutions, including data lakes, data warehouses, SQL databases, and NoSQL databases.Utilize SQL extensively for data profiling, anomaly detection, and data quality checks.Document technical designs, data flows, and operational procedures thoroughly for data quality initiatives.Stay current with emerging trends and technologies in data management, data quality, data observability, Open Telemetry, Open Lineage, and cloud platforms.Collaborate with data architects, data engineers, data scientists, and business stakeholders to integrate data quality processes into existing data pipelines and ensure data readiness for AI and Machine Learning models.Apply expertise in Master Data Management, Data Governance, and Data Catalog concepts to enhance data quality.Troubleshoot and resolve complex data quality issues, identifying root causes and implementing sustainable solutions.Potentially mentor junior team members and contribute to best practices within the data management team. .
