Senior Data Quality Analyst 2

Job Summary:

Leads, develops, and implements project(s) of varying complexity and size in assigned functional area. Partners with business stakeholders to ensure a successful project completion and organizational improved capability to data quality standards.

Key Responsibilities:

Leads the quality and integrity of the data within the assigned functional area. Serves as an expert on data quality tools. Conducts data profiling for data. Develops Key Performance Indicators (KPIs) to measure data quality. Creates and executes action plans to resolve data quality issues. Maintains existing rules and develops new rules to support a robust data quality process. Escalates issues and critical conflicts in a timely fashion. Identifies and mitigates data quality risks. Reports data quality risks and KPI measurement statistics to data governance council. Manages metadata and data standards to ensure adherence, working closely with IT. Coordinates activities of data stewards located within the business, working closely with the business process owner. Supports business data analysis and proposal creation as a member of a data governance council. Guides the business to prepare data. Leads initial data exploration steps (binning, pivoting, summarizing and finding correlations, for example). Catalogues business attributes to enable data discovery. Coaches business to establish and enforce guidelines for data collection, integration and processes. Leads data and systems analysis as required to support data governance processes. Understands data governance roles and processes, and ensures that they are followed. Prepares and presents communications to leaders and stakeholders.

Core Responsibilities Unique to the Role

1) Lead the design, implementation, and continuous evolution of the Enterprise Data Quality Framework across the data ecosystem(ingestion pipelines, data warehouses, semantic layers, and Data Products). Establish scalable standards, policies, controls, and best practices that ensure enterprise data is trusted, governed, certified, and AI-ready.
2)  Establish and operationalize data quality capabilities by implementing automated data profiling, validation, reconciliation, monitoring, observability, issue management, and scorecard processes. Define measurable data quality KPIs, SLAs, and governance mechanisms that improve the accuracy, completeness, consistency, timeliness, validity, and reliability of critical enterprise data assets.
3) Partner with Data Engineering, Solution Engineering, Enterprise Data Management (EDM), Architecture, Product Management, cross functional teams and Business teams to embed Data-as-a-Product quality practices throughout the data lifecycle, including business rule management, metadata standards, lineage validation, certification processes, root cause analysis, and proactive remediation of data quality issues.

Required Skills, Education, or Experience

1) Strong hands-on experience designing, implementing, and leading enterprise Data Quality programs and frameworks, including data profiling, validation, reconciliation, root cause analysis, monitoring, issue management, scorecards, and continuous improvement initiatives across large-scale Data Warehouse, Lakehouse, and Data Product environments.
2) Strong expertise in Data Quality, Data Governance, Metadata Management, Data Lineage, Data Stewardship, Master Data Management (MDM), Business Glossary Management, and Data-as-a-Product practices, with experience establishing quality standards, controls, certification processes, and governance frameworks for enterprise data assets.
3) Proven experience implementing and automating data quality controls across the end-to-end data lifecycle, including Source Systems, Data Ingestion, Raw/Bronze, Curated/Silver, Business/Gold, Semantic Models, and Enterprise Data Products, utilizing capabilities such as data profiling, rule-based validations, reconciliations, anomaly detection, exception management, data observability, and quality KPI monitoring.
4) Hands-on expertise with modern cloud data platforms and data engineering technologies, such as Snowflake, Databricks, SQL, Matillion, dbt, and Power BI, with the ability to design scalable data quality solutions and embed quality controls within enterprise data pipelines.
5) Experience with enterprise Data Quality and Governance platforms, and data observability solutions to implement data quality frameworks, metadata management, lineage, cataloging, certification, and governance capabilities across enterprise data products.

Preferred (Nice to Have) Skills, Education, or Experience

1) Experience implementing data quality frameworks for Supply Chain, Quality, Finance, Product Lifecycle, and other Enterprise Products domains within large-scale Data Modernization or Data-as-a-Product initiatives.

2) Experience applying AI/ML and Data Observability capabilities to Data Quality, including anomaly detection, predictive quality monitoring, automated root cause analysis, AI-ready data certification, and governance controls supporting Analytics, Machine Learning, and GenAI solutions.
       
Education
Bachelor's degree in Computer Science, Information Technology, Data Analytics, Engineering, Information Systems, or related field. Master's degree preferred.

Competencies:
Balances stakeholders - Anticipating and balancing the needs of multiple stakeholders.

Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.

Communicates effectively - Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences.

Customer focus - Building strong customer relationships and delivering customer-centric solutions.

Interpersonal savvy - Relating openly and comfortably with diverse groups of people.

Manages ambiguity - Operating effectively, even when things are not certain or the way forward is not clear.

Data Analytics - Discovers, interprets and communicates qualitative and quantitative data; determines conclusions relying on knowledge of business or functional frameworks; simultaneously applies statistics, data validity, data visualization, and problem solving approaches to effectively extract meaningful patterns and business insights; presents conclusions and outcomes that enable data driven business decisions.

Data Mining - Extracts insights from data by identifying relationships and patterns through use of a suite of data exploration and data visualization techniques to understand the underlying structure of the data and enable sound conclusions upon model building.

Data Modeling - Creates, writes and tests data models, test scripts and build scripts using industry standards and tools, version control, and build and test automation to meet business, technical, security, governance and compliance requirements.

Data Communication and Visualization - Constructs a tale of the business problem, root cause, solution options, and opportunities through illustrating data visually, including reports and dashboards.

Data Governance - Assures definition, ownership, metadata management, and security for Cummins data.

Data Literacy - Expresses data in context, including data sources and constructs, analytical methods and applied techniques; describes the use-case application and resulting value.

Data Profiling - Assesses data issues and cleansing requirements to perform data extraction, mapping, collection, and testing; establishes good, quality data.

Data Quality - Identifies, understands and corrects flaws in data that supports effective information governance across operational business processes and decision making.

Values differences - Recognizing the value that different perspectives and cultures bring to an organization.

Education, Licenses, Certifications:
College, university, or equivalent degree in statistics, information systems or related field required.
This position may require licensing for compliance with export controls or sanctions regulations.

Experience:
Intermediate level of relevant work experience required.