Data Engineer II

Company Summary

EchoStar 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 SQL
• Lead 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 GitLab, 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 M
• Proficiency with Git and GitLab for version control and CI/CD pipeline implementation alongside Infrastructure-as-Code tools like Terraform or AWS CloudFormation
• 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 SQL
  • Amazon EMR and Apache Spark
  • Apache Airflow or Control M
  • GitLab CI/CD pipelines
  • Amazon Q or Generative AI tools

Salary Ranges

Compensation: $83,160.00/Year - $118,800.00/Year
Benefits

We offer versatile health perks, including flexible spending accounts, HSA, a 401(k) Plan with company match, ESPP, career opportunities, and a flexible time away plan; all benefits can be viewed here: EchoStar Benefits.

The base pay range shown is a guideline. Individual total compensation will vary based on factors such as qualifications, skill level, and competencies; compensation is based on the role's location and is subject to change based on work location.

Candidates need to successfully complete a pre-employment screen, which may include a drug test and DMV check. Our company is committed to fostering an inclusive and equitable workplace where every individual has the opportunity to succeed. We are dedicated to providing individuals with criminal or arrest records a fair chance of employment in accordance with local, state, and federal laws.

The posting will be active for a minimum of 3 days. The active posting will continue to extend by 3 days until the position is filled.

We pride ourselves on developing and promoting talent as an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. EchoStar will accommodate the sincerely held religious beliefs of employees if such accommodations are not undue hardships and are otherwise within the bounds of applicable law. All qualified applicants with arrest or conviction records will be considered for employment in accordance with local, state, and federal law. You may redact any information that identifies age, date of birth, or dates of school/graduation from your application documents before submission and throughout our application process.

EchoStar will provide reasonable accommodation to otherwise qualified job applicants and employees with known physical or mental disabilities, unless doing so poses an undue hardship on the Company, poses a direct threat of substantial harm to others, or is otherwise not required by law. EchoStar has a more detailed Accommodation Policy that applies to employees. EchoStar endeavors to make echostar.com and jobs.echostar.com accessible to users. Please contact leaves@dish.com if you would like to discuss the accessibility of our website or need assistance completing the application process. This contact information is for accommodation requests only; do not use this contact information to inquire about the status of applications.

Click the links to access the following statements: EEO Policy Statement, Pay Transparency, EEOC Know Your Rights (English/Spanish)

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 M
• Proficiency with Git and GitLab for version control and CI/CD pipeline implementation alongside Infrastructure-as-Code tools like Terraform or AWS CloudFormation
• 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 SQL
  • Amazon EMR and Apache Spark
  • Apache Airflow or Control M
  • GitLab CI/CD pipelines
  • Amazon Q or Generative AI tools

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 SQL
• Lead 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 GitLab, utilizing Infrastructure-as-Code templates and maintaining strict data governance, security, and quality throughout the lifecycle