AWS Data Engineer - Fully Remote - US Only

Scalepex; Plano (TX); Remote; USD 100,000 - 130,000; Full time

Job summary

A dynamic services firm, Scalepex, is seeking an experienced AWS Data Engineer to build scalable data solutions for leading brands like Nike and Pepsi. The ideal candidate will design and optimize data pipelines, implement ETL processes, and ensure data security in the utilities industry.

Qualifications

• Minimum of 5 years of experience in data engineering.
• Strong programming skills in Python with experience using PySpark and Pandas.
• Hands-on experience with distributed systems and scalable architectures.

Responsibilities

• Develop scalable data pipelines using AWS services like Glue and Redshift.
• Implement ETL/ELT processes to clean and integrate data from multiple sources.
• Ensure data security and compliance with industry regulations.

Skills

Data Engineering

AWS Services

Python

ETL/ELT processes

Analytical Skills

Problem Solving

Tools

PySpark

Pandas

AWS Step Functions

AWS Lambda

AWS Glue

AWS Redshift

AWS S3

Job description

❋ 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