Data Steward

Data Steward

Responsibilities

1. Manage, monitor, and maintain the quality, accuracy, and completeness of operational technology (OT) data streams and telemetry datasets.
2. Define and execute rule-based data validation, cleanup procedures, and anomaly detection logic to resolve corrupted, missing, or duplicated sensor tags.
3. Standardize physical-to-digital asset modeling, schema mapping, and tag naming conventions across industrial assets.
4. Oversee and validate OT data curation, transformation rules, and table definitions within Databricks / Enterprise Data Lakehouse environments.
5. Maintain organized data catalogs, data dictionaries, and metadata documentation for OT and plant data assets.
6. Ensure adherence to enterprise data governance policies, standards, data security, and lifecycle management protocols.
7. Collaborate with engineering, plant operations, IT, and downstream analytics teams to align operational data requirements with business needs.
8. Conduct root-cause analysis on data quality issues and establish alerting workflows to maintain continuous data flow.
9. Support business intelligence, dashboarding, and advanced analytics teams by providing validated, high-fidelity time-series and operational data.

1. Analytical thinker with a structured and methodical approach to problem-solving.
2. High level of integrity, professionalism, and commitment to data quality and precision.
3. Strong communication and interpersonal skills to build effective cross-functional relationships.
4. Adaptable to evolving technologies, changing business needs, and project demands.

Qualifications

1. Degree in Engineering (Instrumentation, Computer Science, Electrical), Data Science, Information Technology, or a related field.
2. Minimum of 3–5 years of hands-on experience in data stewardship, data quality management, or operational data analytics.
3. Strong understanding of industrial telemetry, time-series data, sensor networks, and automation controllers (PLC, DCS, SCADA).
4. Hands-on experience or working familiarity with Databricks (Lakehouse architecture, Delta tables, SQL querying) and cloud data infrastructure.
5. Proficiency in SQL, data validation techniques, exploratory data analysis, and working with large operational datasets.
6. Familiarity with data visualization platforms (such as Tableau, Power BI, Looker, or Grafana) and data cataloguing tools.
7. Demonstrated ability to maintain data integrity, troubleshoot data anomalies, and document data flows effectively.