Lead Data Modeler

Lead Data Modeler

Job Description

Must Have Technical/Functional Skills

• Data modelling, specifically for utility gas and electricity US BusinessExperience in data warehousing

and business intelligence for utilities.

• Knowledge of machine learning applications in predictive maintenance and energy forecasting.

• Familiarity with Big Data technologies (Hadoop, Spark) and real-time streaming (Kafka).

• Expertise in data modeling tools such as Erwin, IBM Data Architect, or PowerDesigner.

• Strong knowledge of utility industry data structures, including metering, customer information,

asset management, and outage management.

• Proficiency in SQL databases (e.g., SQL Server, Oracle, PostgreSQL) and NoSQL technologies

(e.g., MongoDB, Cassandra).

• Experience working with SCADA, AMI (Advanced Metering Infrastructure), GIS, and IoT data.

• Familiarity with cloud-based data platforms (AWS, Azure, or Google Cloud) for utilities.

• Understanding of regulatory compliance and industry standards like CIM, IEC, FERC, NERC, and GDPR.

• Strong analytical, problem-solving, and communication skills.

Roles & Responsibilities

• Develop conceptual, logical, and physical data models for utility-related business domains,

including power generation, transmission, distribution, metering, billing, and customer service.

• Collaborate with business stakeholders and IT teams to understand data requirements

for energy operations, regulatory compliance, and analytics.

• Design and optimize database structures for performance, scalability, and data integrity in

relational (SQL) and NoSQL environments.

• Ensure data accuracy, consistency, and security while aligning with industry standards

such as CIM (Common Information Model), IEC 61968, IEC 61970, and FERC/NERC compliance.

• Support data integration efforts for smart grid systems, SCADA, IoT-enabled devices, GIS,

and customer information systems (CIS).

• Work closely with ETL and data engineering teams to define data pipelines, transformations,

and governance strategies.

• Document data models, metadata, and data dictionaries for enterprise-wide usage.

• Analyze and improve existing data models to enhance operational efficiency and reporting

capabilities.

• Stay updated on emerging trends in utility data modeling, such as distributed energy

• resources (DER), smart metering, and predictive analytics.