Data Engineer II

Company Summary Echo Star  is reimagining the future of connectivity. Our business reach spans satellite television service, live-streaming and on-demand programming, smart home installation services, mobile plans and products.

Today, our brands include Boost Mobile, DISH TV, Gen Mobile, Hughes and Sling TV. Department Summary Our Technology teams challenge the status quo and reimagine capabilities across industries. Whether through research and development, technology innovation or solution engineering, our team members play a vital role in connecting consumers with the products and platforms of tomorrow.

Job Duties and Responsibilities Candidates must be willing to participate in at least one in-person interview.

This role addresses the complex challenges of constructing and optimizing high-performance data pipelines for both batch and streaming data processing across diverse internal and external sources. The position drives enterprise efficiency by leading critical Proof-of-Concept initiatives to evaluate and implement modern workflow orchestration tools and pioneering the integration of Generative AI services. Success means establishing reliable, scalable cloud infrastructure and robust automation that ensures data governance, quality, and optimal cost-effectiveness across the entire data lifecycle.

What Success Looks Like (Objectives):

Design, construct, and optimize scalable data pipelines for batch and streaming data processing from various internal and external sources

Develop and manage data processing jobs using Apache Spark on Amazon EMR clusters while ensuring performance, cost-efficiency, and scalability

Implement transformation logic and complex data workflows primarily using Python, PySpark, and SQLLead a Proof-of-Concept project evaluating Apache Airflow as the enterprise-wide workflow orchestration tool, designing and deploying Directed Acyclic Graphs to manage dependencies

Explore and implement integration points for Amazon Q into data pipelines as part of the orchestration POC for automated data quality checks, data documentation generation, or pipeline optimization

Implement CI/CD pipelines for data platform components using Git Lab, utilizing Infrastructure-as-Code templates and maintaining strict data governance, security, and quality throughout the lifecycle

Skills, Experience and Requirements Core Skills and Competencies (What you’ll bring):

Expert-level proficiency in Python, PySpark, and SQL alongside advanced Spark programming and monitoring of Big Data data engineering jobs

Deep practical experience leveraging AWS Big Data services, specifically Amazon EMR, EC2, S3, and modern workflow orchestrators like Apache Airflow or Control MProficiency with Git and Git Lab for version control and CI/CD pipeline implementation alongside Infrastructure-as-Code tools like Terraform or AWS Cloud

Formation AI Literacy and Innovation through the application of Generative AI tools like Amazon Q to optimize data engineering workflows, automated quality checks, and pipeline documentation

Strong analytical, problem-solving, and communication skills to effectively present findings from Proof-of-Concept initiatives and collaborate within Agile teams

Experience with Databricks preferred

Minimum Requirements:

Minimum Education:

Bachelor’s Degree in Computer Science, Data Engineering, or a related technical field

Minimum Experience:

2 years of experience in Big Data and Data Engineering Required Technical

Skills:

Must have at least 2 years of experience with:

Python and SQLAmazon EMR and Apache Spark Apache Airflow or Control MGitLab CI/CD pipelines

Amazon Q or Generative AI tools

Visa sponsorship not available for this role.#LI-CH2

Salary Ranges Compensation: $83,160.00/Year - $/Year Benefits We offer…