Data Steward

Work Schedule

Environmental Conditions

Job Description

Job Summary

We are seeking an experienced and detail-orientedData Steward to drive the execution of enterprise data governance and data quality initiatives. This role operates as anindependent contributor, responsible for ensuring that data assets are trusted, well-governed, and accessible across the organization.

The Data Steward willown the governance, lineage, and quality of data assets across the full data lifecycle—from ERP source systems through RAW, consumable, and KPI layers. Leveragingdata.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 toestablish 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 andhands-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 fromERP → RAW → consumable → KPI layers, ensuring traceability, transparency, and alignment with governance standards.

• Maintain and curate data assets withindata.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 toidentify and steward critical data elements (CDEs) across all layers.

• Establish and standardizebusiness definitions, metrics, and KPIs, contributing to a governedsemantic layer for analytics and AI.

• Improvedata 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 maintaindata quality rules, validations, controls, and scorecards, aligned to transformation layers.

• UtilizeSQL 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 tovalidate transformation logic and ensure reliability of KPI outputs and AI datasets.

• Establishproactive monitoring and automated checks to detect and prevent data defects early in the pipeline.

Data Catalog & Metadata Enablement (data.world)

• Serve as aprimary steward of the data.world platform, ensuring high-quality metadata, lineage mapping, and usability of cataloged assets.

• Document and maintainend-to-end lineage relationships within data.world, connecting ERP sources to downstream datasets and KPI layers.

• Leveragedata.world APIs and integrations to supportautomation of metadata ingestion, lineage updates, and catalog curation.

• Enablesemantic consistency within the data catalog, ensuring alignment between technical data and business meaning.

• Driveadoption of data.world by enabling self-service data discovery and trusted data usage.

• Providetraining, 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 toalign data definitions, transformations, and KPI logic.

• Translate business requirements intogoverned data models, semantic definitions, and quality controls.

• Work independently while influencing stakeholders toadopt standardized definitions, governance practices, and trusted data sources.

• Applyanalytical thinking and domain expertise to resolve inconsistencies and improve data processes.

• Maintain comprehensive documentation ofdata flows, lineage, semantic definitions, and governance controls.

• Contribute to thecontinuous improvement and maturity of data governance practices, particularly in support of AI and advanced analytics.

Preferred Experience

• Hands-on experience withdata.world or similar modern data catalog platforms.

• Strong understanding ofdata governance frameworks (e.g., DAMA-DMBOK).

• Experience managingdata lineage and quality across multi-layered architectures (ERP, data lakes, transformation layers, KPI/reporting).

• Experience supporting or buildingsemantic layers for BI and/or AI use cases.

• Proficiency inSQL and Python for data analysis, profiling, and automation of data quality checks.

• Experience working withAPIs or programmatic interfaces for metadata and catalog automation.

• Familiarity withmodern data platforms (Databricks, Redshift, Athena).

• Experience withdata visualization tools (e.g., Power BI).

• Knowledge ofdata 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 indata stewardship, data governance, or data quality roles.

• Demonstrated experience working withdata catalogs, metadata management, and lineage.

• Strong problem-solving skills with the ability towork independently and manage moderately complex data challenges.

• Excellent communication and stakeholder management skills, with the ability toinfluence and drive adoption of governance practices.

• Comfortable workinghands-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 acrossERP → KPI pipeline

• High adoption and effective use ofdata.world

• Consistent and governedbusiness definitions and semantic layer

• Measurable improvements indata quality KPIs

• Reliable,AI-ready datasets supporting analytics and decision-making