Staff Data Engineer, Applied Data Science III
New
J
JobgetherUtility energy
Remote work within the United States.Full-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 4–5 years of relevant professional experience, including experience in the utility sector
- Required Skills
- PythonSQLGitMachine LearningDatabricks
Requirements
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a closely related quantitative discipline.
- 4–5 years of relevant professional experience, including experience in the utility sector.
- Hands-on contributions to data engineering, applied data science, and data architecture or integration design.
- Demonstrated experience building and maintaining repeatable data pipelines and integrations.
- Experience designing data models, schemas, relationships, and source-to-target mappings.
- Strong proficiency in Python and SQL, including maintainable programming, complex queries, data transformation, and troubleshooting.
- Experience with pipeline testing, scheduling or orchestration, logging, data-quality validation, and failure investigation.
- Practical experience with at least one cloud or distributed-data platform, such as Databricks, Microsoft Azure, AWS, or Google Cloud.
- Strong understanding of probability, sampling, statistical inference, regression, confidence and prediction intervals, and uncertainty.
- Experience developing and evaluating statistical or machine learning models using appropriate validation methods and interpretation techniques.
- Familiarity with Git, code reviews, software testing, technical documentation, and reproducible analytical workflows.
- Ability to explain technical designs, analytical findings, assumptions, and limitations to technical and non-technical audiences.
Responsibilities
- Explore and assess utility datasets, including AMI interval data, SCADA, GIS, asset information, customer programs, and billing records.
- Design, develop, test, and maintain repeatable ETL/ELT pipelines that ingest, transform, reconcile, and deliver data.
- Build reusable Python and SQL components for data cleansing, standardization, aggregation, validation, and feature preparation.
- Profile source data and implement automated quality checks for completeness, validity, consistency, uniqueness, and freshness.
- Develop record-matching and entity-resolution logic for customer, asset, device, program, and operational records.
- Translate business, analytical, and operational requirements into documented data architecture and integration designs.
- Develop data models, schemas, data dictionaries, entity relationship diagrams, and source-to-target mappings.
- Develop and validate forecasting, classification, anomaly detection, clustering, and prioritization models using utility datasets.
- Monitor model performance and support documented recalibration, retraining, and ongoing model updates.
- Prepare technical reports, process maps, architecture documentation, and analytical presentations.
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