**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 Thermo Fisher Scientific is an EEO/Affirmative Action Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other legally protected status.

📌 Data Steward (Tijuana)
🏢 ThermoFisher Scientific
📍 Tijuana