Responsibilities

Key Responsibilities

- Manage, monitor, and maintain the quality, accuracy, and completeness of operational technology (OT) data streams and telemetry datasets.

- Define and execute rule-based data validation, cleanup procedures, and anomaly detection logic to resolve corrupted, missing, or duplicated sensor tags.

- Standardize physical-to-digital asset modeling, schema mapping, and tag naming conventions across industrial assets.

- Oversee and validate OT data curation, transformation rules, and table definitions within Databricks / Enterprise Data Lakehouse environments.

- Maintain organized data catalogs, data dictionaries, and metadata documentation for OT and plant data assets.

- Ensure adherence to enterprise data governance policies, standards, data security, and lifecycle management protocols.

- Collaborate with engineering, plant operations, IT, and downstream analytics teams to align operational data requirements with business needs.

- Conduct root-cause analysis on data quality issues and establish alerting workflows to maintain continuous data flow.

- Support business intelligence, dashboarding, and advanced analytics teams by providing validated, high-fidelity time-series and operational data.

Personal Attributes

- Analytical thinker with a structured and methodical approach to problem-solving.





- High level of integrity, professionalism, and commitment to data quality and precision.

- Strong communication and interpersonal skills to build effective cross-functional relationships.

- Adaptable to evolving technologies, changing business needs, and project demands.

Qualifications

Required Skills &

Qualifications

- Degree in Engineering (Instrumentation, Computer Science, Electrical), Data Science, Information Technology, or a related field.

- Minimum of 3–5 years of hands-on experience in data stewardship, data quality management, or operational data analytics.

- Solid understanding of industrial telemetry, time-series data, sensor networks, and automation controllers (PLC, DCS, SCADA).

- Hands-on experience or working familiarity with Databricks (Lakehouse architecture, Delta tables, SQL querying) and cloud data infrastructure.

- Proficiency in SQL, data validation techniques, exploratory data analysis, and working with large operational datasets.

- Familiarity with data visualization platforms (such as Tableau, Power BI, Looker, or Grafana) and data cataloguing tools.

- Demonstrated ability to maintain data integrity, troubleshoot data anomalies, and document data flows effectively.

📌 Data Steward (Ahmedabad)
🏢 Adani Enterprises
📍 Ahmedabad