Data Steward - Operational Technology Data Management

Adani Enterprises Ltd; Ahmedabad District; On-site; INR 700,000 - 1,100,000; Full time

Job summary

Adani Enterprises Ltd. is seeking a Data Steward to ensure quality and integrity of OT data streams and telemetry datasets.You will define validation rules, cleanse data, and align asset models across industrial assets while coordinating with engineering and IT teams.

The role requires hands-on experience with Databricks, SQL, and data visualization tools to support BI and analytics initiatives with high-fidelity data.

Qualifications

• 3–5 years hands-on experience in data stewardship, data quality management, or operational data analytics.
• Experience with industrial telemetry, time-series data, sensor networks, and automation systems (PLC, DCS, SCADA).
• Proficiency in Databricks and cloud data infrastructure to manage Lakehouse environments.

Responsibilities

• Manage quality, accuracy, and completeness of OT data streams and telemetry datasets.
• Define and execute rule-based data validation and anomaly detection to resolve corrupted or missing tags.
• Standardize asset modeling, schema mapping, and tag naming across assets.
• Oversee data curation, transformation rules, and table definitions in Databricks/EDL environments.
• Maintain data catalogs, dictionaries, and metadata documentation for OT data assets.
• Ensure adherence to data governance, security, and lifecycle policies.
• Collaborate with engineering, plant operations, IT, and analytics teams to align data needs with business goals.
• Conduct root-cause analysis on data quality issues and set up alerting workflows.
• Support BI, dashboards, and analytics teams with validated high-fidelity data.

Skills

Data quality management

Time-series data

SQL proficiency

Data governance

Education

Engineering degree (Instrumentation, CS, Electrical) or related field

Tools

Databricks

Delta tables

SQL querying

Tableau

Power BI

Looker

Grafana

Data cataloguing tools

Job description

Data Steward 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.

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.
• Strong 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.

Experience Level Mid Level