Hybrid: Senior Data Engineer with MDM

We are seeking a highly skilled and experienced Senior Data Engineer specializing in Master Data Management (MDM) to join our data team. The ideal candidate will have a strong background in designing, implementing, and managing end-to-end MDM solutions, preferably within the financial sector. Candidate will be responsible for architecting robust data platforms, evaluating MDM tools, and aligning data strategies to meet business needs.

• Lead the design, development, and deployment of comprehensive MDM solutions across the organization, with an emphasis on financial data domains.
• Demonstrate extensive experience with multiple MDM implementations, including platform selection, comparison, and optimization.
• Architect and present end-to-end MDM architectures, ensuring scalability, data quality, and governance standards are met.
• Evaluate various MDM platforms (e.g., Informatica, Reltio, Talend, IBM MDM, etc.) and provide objective recommendations aligned with business requirements.
• Collaborate with business stakeholders to understand reference data sources and develop strategies for managing reference and master data effectively.
• Implement data integration pipelines leveraging modern data engineering tools and practices.
• Develop, automate, and maintain data workflows using Python, Airflow, or Astronomer.
• Build and optimize data processing solutions using Kafka, Databricks, Snowflake, Azure Data Factory (ADF), and related technologies.
• Design microservices, especially utilizing GraphQL, to enable flexible and scalable data services.
• Ensure compliance with data governance, data privacy, and security standards.
• Support CI/CD pipelines for continuous integration and deployment of data solutions.

• 12+ years of experience in data engineering, with a proven track record of MDM implementations, preferably in the financial services industry.
• Extensive hands-on experience designing and deploying MDM solutions and comparing MDM platform options.
• Strong functional knowledge of reference data sources and domain-specific data standards.
• Expertise in Python, Pyspark, Kafka, microservices architecture (particularly GraphQL), Databricks, Snowflake, Azure Data Factory, SQL, and orchestration tools such as Airflow or Astronomer.
• Familiarity with CI/CD practices, tools, and automation pipelines.
• Ability to work collaboratively across teams to deliver complex data solutions.
• Experience with financial systems (capital markets, credit risk, and regulatory compliance applications).

• Familiarity with financial data models and regulatory requirements.
• Experience with Azure cloud platforms
• Knowledge of data governance, data quality frameworks, and metadata management.