AWS Data Engineer - Fully Remote - US Only
At a glance
Mid-level Data Engineering role at Scalepex. Remote, United States · full-time · no salary stated · we estimate $112,500–$160,000 across the United States.
Scalepex has not published a salary for this role. The figure below is our estimate, not theirs.
dataroles summary, based on the employer's posting.
What you'll do
- Build AWS pipelines that process and reshape utility data from sources such as smart meters and energy grids
- Coordinate pipeline steps with Step Functions, or use Airflow where appropriate
- Use Python, PySpark and Pandas to clean and combine data from multiple sources
- Create utility-focused data models, including ones for energy consumption forecasting, and improve pipeline speed and availability
- Apply security controls to utility data and maintain compliance with relevant regulations
What you bring
- At least five years working in data engineering
- You’re proficient with AWS Step Functions, Lambda, Glue, S3, DynamoDB and Redshift
- You can process large datasets with Python, PySpark and Pandas, and have practical distributed-systems experience
- You can work with ETL/ELT processes and scalable architectures for integrating datasets
- You can obtain and keep the clearance required for this role
Who this fits
This role suits a mid-level data engineer who can work independently and communicate with business stakeholders and cross-functional teams. It is fully remote and limited to candidates in the US.
From the employer
❋ Why Scalepex?
Scalepex is a dynamic services firm specializing in providing solutions for premium brands like Nike, Pepsi, Toyota, Virgin and Walgreens. Our mission is to connect prominent market leaders with top-tier professionals from around the world, fostering collaboration, efficiency, and growth.
❋ Take your portfolio to the next level by working with one of our fastest growing clients.
Join the Innovation Frontier at Scalepex!
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 processes for integrating diverse datasets into centralized systems.
- Familiarity with utilities-specific datasets (e.g., smart meters, energy grids) is highly desirable.
- Strong analytical skills with the ability to work on unstructured datasets.
- Knowledge of data governance practices to ensure accuracy, consistency, and security of data.
- Strong experience in AWS data engineering
- Ability to work independently
- Ability to work with a cross-functional teams, including interfacing and communicating with business stakeholders
- Professional oral and written communication skills
- Strong problem solving and troubleshooting skills with experience exercising mature judgement
- Excellent teamwork and interpersonal skills
- Ability to obtain and maintain the required clearance for this role
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