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About the Role
We are seeking an experienced AWS Data Engineer with a strong background in building scalable data solutions and expertise in utilities-related datasets. The ideal candidate will have at least 5 years of experience in data engineering, a deep understanding of distributed systems, and proficiency with AWS services and tools like Step Functions, Lambda, Glue, and Redshift. This role will focus on designing, developing, and optimizing data pipelines to support analytics and decision-making in the utilities industry.
Key Responsibilities
- Design and Build Data Pipelines : Develop scalable, reliable data pipelines using AWS services (e.g., Glue, S3, Redshift) to process and transform large datasets from utility systems like smart meters or energy grids.
- Workflow Orchestration : Use AWS Step Functions to orchestrate workflows across data pipelines; experience with Airflow is acceptable but Step Functions is preferred.
- Data Integration and Transformation : Implement ETL/ELT processes using PySpark, Python, and Pandas to clean, transform, and integrate data from multiple sources into unified datasets.
- Distributed Systems Expertise : Leverage experience with complex distributed systems to ensure reliability, scalability, and performance in handling large-scale utility data.
- Serverless Application Development : Use AWS Lambda functions to build serverless solutions for automating data processing tasks.
- Data Modeling for Analytics : Design data models tailored for utilities use cases (e.g., energy consumption forecasting) to enable advanced analytics
- Optimize Data Pipelines : Continuously monitor and improve the performance of data pipelines to reduce latency, enhance throughput, and ensure high availability.
- Ensure Data Security and Compliance : Implement robust security measures to protect sensitive utility data and ensure compliance with industry regulations.
Requirements
Required Qualifications
- Minimum of 5 years of experience in data engineering
- Proficiency in AWS services such as Step Functions, Lambda, Glue, S3, DynamoDB, and Redshift.
- Strong programming skills in Python with experience using PySpark and Pandas for large-scale data processing.
- Hands-on experience with distributed systems and scalable architectures.
- Knowledge of ETL/ELT pro