Data Governance Tech Lead

Salary: open + bonus

Location: Chicago, IL

Hybrid: 3 days onsite, 2 days remote

*We are unable to provide sponsorship for this role*

Qualifications

• Bachelor's degree
• 7+ years of experience in data governance disciplines: metadata management; data quality analysis; data quality remediation; data profiling and data lineage.
• Experience with Data Governance tools such as Collibra, IBM Infosphere Information Server Suite or Informatica.
• Proficient with SQL.
• Experience with Databases (ie Oracle, SQL Server, DB2, Amazon Redshift, NoSQL, Object-based) and ETL tools.
• Previous experience in designing and building data capabilities like data quality, metadata, data lineage, data catalog and data dictionary.
• Experience with Business Intelligence/Reporting tools such as Tableau/Cognos.
• Financial Services industry experience
• DAMA certified (preferred)

Responsibilities

• Create, develop, configure and execute end to end business and technical data lineage across disparate sources working with business and technical SMEs/developers understanding the applications system/technical design and create data flow diagrams/data mappings.
• Experience working on metadata management ingesting metadata using metadata bridges/connectors, creating and ingesting custom source to target mappings, custom metadata assets.
• Develop Metadata, Data Lineage and Data Quality Solutions for multiple data sources across On Prem and Cloud environments including but not limited to Kafka, Protocol Buffers, REDIS, APIs, Databases, Flat Files, JSON, ETL/BI Tools and other Data Platform technologies etc.
• Design, Build and Execute Data Quality Rules over the CDEs identified according to the business needs to ensure clean and healthy data.
• Manage Data Quality Exceptions and help the team to find out the root cause analysis.
• Implement data governance policies, procedures, controls, and standards.
• Hands-on experience working on data quality and data governance technology and tools.
• Utilize data profiling and data quality tools to analyze and determine causes of data quality issues.
• Design and implement Data Quality Dashboards for monitoring and reporting.