Data Engineer

Essential Responsibilities:

• Design and maintain database architectures, schemas, and data models tailored to grid innovation and energy system applications.
• Utilize efficient data storage technologies (e.g., Relational Databases, Data Lakes, NoSQL) to ensure scalable and secure data access.
• Build, optimize, and maintain reliable data pipelines for data ingestion, cleaning, transformation, and feature extraction from structured and unstructured sources.
• Develop and manage integrations with internal and external data sources and APIs to enable seamless data flow.
• Identify new and relevant datasets to improve product capabilities and decision-making across the business.
• Automate data integration and transformation workflows for diverse data formats and operational needs.
• Monitor performance and scalability of data systems and implement enhancements to increase efficiency and reliability.
• Apply data governance policies and implement data quality checks to ensure data integrity across systems.
• Collaborate with Application Architects, Data Scientists, ML Engineers, and other technical stakeholders to deliver relevant, ready-to-use datasets.
• Work closely with product management and R&D teams to gather requirements and develop innovative data solutions that support product development.

Must-Have Requirements

• PhD, Master’s, or Bachelor’s degree in Data Science, Computer Science, Electrical Engineering, or a related field with a focus on data engineering.
• Proven experience in the energy, smart infrastructure, or industrial automation sectors, with hands-on project experience in building and managing data pipelines, typically acquired through a minimum of 5 years of service.

• Proficiency in Python, SQL, and at least one other programming language commonly used in data engineering (e.g., Scala, Java).
• Experience with relational databases (e.g., PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra) and data warehousing technologies like Snowflake or Redshift.
• Familiarity with cloud platforms like AWS, Azure, or GCP for deploying and managing data systems.
• Extensive experience with ETL processes (Extract, Transform, Load) and automating data pipeline workflows.
• Experience with data visualization tools such as Tableau, Power BI, or similar platforms for building reports and dashboards.
• Familiarity with big data tools and technologies, such as Hadoop, Kafka, and Spark.
• Ability to collaborate effectively in a team environment, contributing ideas and taking initiative to solve problems.
• Adaptability to work in a dynamic, multi-tasking environment, with the ability to address evolving challenges.
• Effective communication skills, with the ability to collaborate smoothly with cross-functional teams and resolve conflicts proactively.

Nice-to-Have Requirements:

• Familiarity with data governance frameworks and validation standards in the energy sector.
• Knowledge of distributed computing environments and model deployment at scale.

Additional Information