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.
- 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.
