Data Steward

**Work Schedule** Standard (Mon-Fri) **Environmental Conditions** Office **Job Description** **Job Summary** We are seeking an experienced and detail-oriented **Data Steward** to drive the execution of enterprise data governance and data quality initiatives. This role operates as an **independent contributor**, responsible for ensuring that data assets are trusted, well-governed, and accessible across the organization. The Data Steward will **own the governance, lineage, and quality of data assets across the full data lifecycle—from ERP source systems through RAW, consumable, and KPI layers**. Leveraging **data.world** as the enterprise data catalog, this role will ensure strong metadata management, lineage transparency, and data discoverability. A key focus of this role is to **establish a consistent,** **trusted semantic layer that supports analytics and AI use cases**, ensuring data is clearly defined, standardized, and ready for downstream consumption. The role requires a balance of governance expertise and **hands-on technical capability (SQL and Python)** to validate data, enforce quality, and support metadata and lineage automation. **Key Responsibilities** **Data Governance, Lineage & Stewardship** * **Own and manage end-to-end data lineage** from **ERP → RAW → consumable → KPI layers**, ensuring traceability, transparency, and alignment with governance standards. * Maintain and curate data assets within **data.world**, ensuring datasets are accurately classified, documented, and contextually enriched. * **Define, implement, and enforce metadata standards**, including business definitions, lineage, and transformation logic across the data pipeline. * Partner with business and technical stakeholders to **identify and steward critical data elements (CDEs)** across all layers. * Establish and standardize **business definitions, metrics, and KPIs**, contributing to a governed **semantic layer for analytics and AI**. * Improve **data discoverability, lineage visibility, and contextual clarity** to enable trusted data usage. **Data Quality Management (End-to-End Pipeline)** * **Own data quality across the full data pipeline (ERP → RAW → consumable → KPI)**, ensuring consistency, accuracy, and completeness at each stage. * Develop and maintain **data quality rules, validations, controls, and scorecards**, aligned to transformation layers. * Utilize **SQL and Python scripting** to perform data profiling, validation, reconciliation, and anomaly detection. * **Identify, analyze, and lead resolution of data quality issues**, performing root cause analysis across upstream and downstream systems. * Collaborate with engineering and business teams to **validate transformation logic and ensure reliability of KPI outputs and AI datasets**. * Establish **proactive monitoring and automated checks** to detect and prevent data defects early in the pipeline. **Data Catalog & Metadata Enablement (data.world)** * Serve as a **primary steward of the data.world platform**, ensuring high-quality metadata, lineage mapping, and usability of cataloged assets. * Document and maintain **end-to-end lineage relationships within data.world**, connecting ERP sources to downstream datasets and KPI layers. * Leverage **data.world APIs and integrations** to support **automation of metadata ingestion, lineage updates, and catalog curation**. * Enable **semantic consistency within the data catalog**, ensuring alignment between technical data and business meaning. * Drive **adoption of data.world** by enabling self-service data discovery and trusted data usage. * Provide **training, guidance, and support** to stakeholders on catalog usage, lineage interpretation, and governance best practices. **Cross-Functional Collaboration & Influence** * Collaborate with business, analytics, data engineering, and AI/ML teams to **align data definitions, transformations, and KPI logic**. * Translate business requirements into **governed data models, semantic definitions, and quality controls**. * Work independently while influencing stakeholders to **adopt standardized definitions, governance practices, and trusted data sources**. * Apply **analytical thinking and domain expertise** to resolve inconsistencies and improve data processes. * Maintain comprehensive documentation of **data flows, lineage, semantic definitions, and governance controls**. * Contribute to the **continuous improvement and maturity of data governance practices**, particularly in support of AI and advanced analytics. **Preferred Experience** * Hands-on experience with **data.world** or similar modern data catalog platforms. * Strong understanding of **data governance frameworks** (e.g., DAMA-DMBOK). * Experience managing **data lineage and quality across multi-layered architectures** (ERP, data lakes, transformation layers, KPI/reporting). * Experience supporting or building **semantic layers for BI and/or AI use cases**. * Proficiency in **SQL and Python for data analysis, profiling, and automation of data quality checks**. * Experience working with **APIs or programmatic interfaces** for metadata and catalog automation. * Familiarity with **modern data platforms** (Databricks, Redshift, Athena). * Experience with **data visualization tools** (e.g., Power BI). * Knowledge of **data privacy and regulatory standards** (e.g., GDPR, CCPA). **Qualifications** * Bachelor’s degree in computer science, Information Systems, Data Science, or related field. * 3+ years of experience in **data stewardship, data governance, or data quality roles**. * Demonstrated experience working with **data catalogs, metadata management, and lineage**. * Strong problem-solving skills with the ability to **work independently and manage moderately complex data challenges**. * Excellent communication and stakeholder management skills, with the ability to **influence and drive adoption of governance practices**. * Comfortable working **hands-on with data using SQL and Python** to validate data, enforce quality rules, and support governance processes. **What Success Looks Like** * Trusted, well-documented data assets across **ERP → KPI pipeline** * High adoption and effective use of **data.world** * Consistent and governed **business definitions and semantic layer** * Measurable improvements in **data quality KPIs** * Reliable, **AI-ready datasets** supporting analytics and decision-making