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Work ScheduleStandard (Mon-Fri)

Environmental ConditionsOfficeJob Description

Job SummaryWe 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 ResponsibilitiesData Governance, Lineage & StewardshipOwn 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 & InfluenceCollaborate 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 ExperienceHands-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).

QualificationsBachelor’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 LikeTrusted, 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

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📌 Data Steward (Ciudad de México)
🏢 Thermo Fisher Scientific
📍 Ciudad de México