Data Engineer
About The Position
Green Mountain Power Corporation seeks a Data Engineer to collect, clean, and analyze large, complex datasets from multiple sources, including APIs, data streams, and databases, to generate actionable insights. Build, validate, and deploy machine learning models and algorithms using best practices in data science and reproducible research. Apply statistical modeling, machine learning, and predictive analytics to solve business problems and improve decision-making. Analyze source systems, define data transformation requirements, and design suitable data models. Collaborate with the Data & analytics team to ensure data quality, resolve data-related challenges, and identify opportunities for new data-driven solutions. Implement data privacy, security, and compliance best practices when handling sensitive or regulated data. Continuously evaluate and adopt new methods, tools, and techniques to improve the accuracy, efficiency, and scalability of analytics solutions. Support both Agile and Waterfall project methodologies while delivering models, insights, and recommendations on time. Telecommuting permitted. 5% domestic travel required.
Requirements
- Bachelor's degree (or foreign equivalent) in Computer Science, Information Technology, Biotechnology, or a related field.
- 7 years of experience in data or software engineering.
- Programming Languages: PL/SQL, Java, Python, HTML, Salesforce SOQL, Unix Shell Scripting
- ETL & BI Tools: Talend (Cloud & ESB), Oracle Data Integrator, Informatica, Apache Airflow, Azure Data Factory, OBIEE, Tableau, and Power BI
- Database Technologies: Snowflake, Oracle, SQL Server, PostgreSQL, MongoDB, Hadoop, Spark, Hive, Impala, and HDFS
- Cloud Platforms: AWS S3, Microsoft Azure, and Google Cloud Platform (GCP)
- Data Modeling & Design: Star Schema, Snowflake Schema, and Dimensional Modeling
- Version Control: Git, GitHub, and GitLab
- Data Integration & APIs: RESTful APIs, WSDL, JSON, XML, Postman, and SoapUI
- API Development & Management: Snowflake Streamlit, microservices architecture, and cursor-based operations
- Security & Compliance: Active Directory, SSL/TLS, encryption, and data governance standards
- Operating Systems: Windows, Linux, UNIX, and Mainframes
- Project Management Tools: JIRA, Monday.com
- Development Methodologies: Agile and Waterfall
Responsibilities
- Collect, clean, and analyze large, complex datasets from multiple sources, including APIs, data streams, and databases, to generate actionable insights.
- Build, validate, and deploy machine learning models and algorithms using best practices in data science and reproducible research.
- Apply statistical modeling, machine learning, and predictive analytics to solve business problems and improve decision-making.
- Analyze source systems, define data transformation requirements, and design suitable data models.
- Collaborate with the Data & analytics team to ensure data quality, resolve data-related challenges, and identify opportunities for new data-driven solutions.
- Implement data privacy, security, and compliance best practices when handling sensitive or regulated data.
- Continuously evaluate and adopt new methods, tools, and techniques to improve the accuracy, efficiency, and scalability of analytics solutions.
- Support both Agile and Waterfall project methodologies while delivering models, insights, and recommendations on time.
Benefits
- Employee Referral Program
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What This Job Offers
Job Type
Full-time
Career Level
Senior
