The Data Engineer II will maintain CI/CD pipelines, develop AI/ML platforms, process telecom data, and optimize data storage and retrieval strategies.
Company Summary
EchoStar is reimagining the future of connectivity. Our business spans satellite television service, streaming and on-demand programming, smart home installation services, 5G wireless consumer and commercial services, internet and other enterprise products.
Today, our brands include EchoStar, Hughes, DISH TV, Sling TV, Boost Infinite, Boost Mobile, DISH Wireless, OnTech and GenMobile.
Job Duties and Responsibilities
Data Engineer II sought by DISH Wireless in Littleton, CO.
Maintain a CI/CD pipeline for our data software to ensure we keep quality high and time to market low. Develop, deploy, automate and maintain next generation AI/ML platform and pipelines. Ingest and process streaming data from telecom sources (5G/4G network logs, IoT sensors, edge devices) using platforms to enable near-real-time AI analytics. Lead large scale, data driven initiatives in order to drive enhanced visibility and quality of the wireless network. Design, build, and maintain scalable data pipelines to collect, process, and integrate large-scale telecom data (e.g., call detail records, network telemetry, OSS/BSS data, customer usage patterns). Implement feature engineering pipelines for AI/ML models, collaborating closely with data scientists to operationalize features for model training and inference. Optimize data storage and retrieval strategies for high-volume, high-velocity telecom datasets across cloud and edge environments. Automate infrastructure provisioning and orchestration for data and AI workloads using CI/CD pipelines and Infrastructure as Code (Terraform, CloudFormation).Monitor, troubleshoot, and optimize data pipelines to ensure low-latency, high-throughput data flows for AI-driven telecom applications.
Skills, Experience and Requirements
Requires: Bachelor's degree (or foreign equivalent) in Computer Science, Computer Engineering, or a closely related field plus 2 years of experience in job offered or similar roles. Also requires 2 years of experience with the following (which may have been gained concurrently): Kafka, Spark Streaming, PostgreSQL, NoSQL (MongoDB), ETL (Extract, Transform, Load) concepts, Python, Airflow, Source control Systems (Github), CICD Tools (Github CICD), Linux-like systems, AI Model Management Systems (Amazon Bedrock or Sagemaker).
Salary Ranges
Compensation: $116,688.00/Year
Benefits
Employment is contingent on successful completion of a pre-employment criminal background check, which may include a drug test.
Rate of Pay: $116,688.00/Year
Benefits information available at careers.dish.com.
Apply at careers.dish.com. Ref: 2026-98817 if applying externally through careers.dish.com; Ref: 2026-98816 if applying internally. May also apply by emailing resume with (Ref: 2026-98817) to [email protected]. 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.
EchoStar is reimagining the future of connectivity. Our business spans satellite television service, streaming and on-demand programming, smart home installation services, 5G wireless consumer and commercial services, internet and other enterprise products.
Today, our brands include EchoStar, Hughes, DISH TV, Sling TV, Boost Infinite, Boost Mobile, DISH Wireless, OnTech and GenMobile.
Job Duties and Responsibilities
Data Engineer II sought by DISH Wireless in Littleton, CO.
Maintain a CI/CD pipeline for our data software to ensure we keep quality high and time to market low. Develop, deploy, automate and maintain next generation AI/ML platform and pipelines. Ingest and process streaming data from telecom sources (5G/4G network logs, IoT sensors, edge devices) using platforms to enable near-real-time AI analytics. Lead large scale, data driven initiatives in order to drive enhanced visibility and quality of the wireless network. Design, build, and maintain scalable data pipelines to collect, process, and integrate large-scale telecom data (e.g., call detail records, network telemetry, OSS/BSS data, customer usage patterns). Implement feature engineering pipelines for AI/ML models, collaborating closely with data scientists to operationalize features for model training and inference. Optimize data storage and retrieval strategies for high-volume, high-velocity telecom datasets across cloud and edge environments. Automate infrastructure provisioning and orchestration for data and AI workloads using CI/CD pipelines and Infrastructure as Code (Terraform, CloudFormation).Monitor, troubleshoot, and optimize data pipelines to ensure low-latency, high-throughput data flows for AI-driven telecom applications.
Skills, Experience and Requirements
Requires: Bachelor's degree (or foreign equivalent) in Computer Science, Computer Engineering, or a closely related field plus 2 years of experience in job offered or similar roles. Also requires 2 years of experience with the following (which may have been gained concurrently): Kafka, Spark Streaming, PostgreSQL, NoSQL (MongoDB), ETL (Extract, Transform, Load) concepts, Python, Airflow, Source control Systems (Github), CICD Tools (Github CICD), Linux-like systems, AI Model Management Systems (Amazon Bedrock or Sagemaker).
Salary Ranges
Compensation: $116,688.00/Year
Benefits
Employment is contingent on successful completion of a pre-employment criminal background check, which may include a drug test.
Rate of Pay: $116,688.00/Year
Benefits information available at careers.dish.com.
Apply at careers.dish.com. Ref: 2026-98817 if applying externally through careers.dish.com; Ref: 2026-98816 if applying internally. May also apply by emailing resume with (Ref: 2026-98817) to [email protected]. 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.
Similar Jobs
Aerospace • Cloud • Digital Media • Information Technology • Mobile • News + Entertainment • Generative AI
Transform complex data requirements into scalable AI infrastructure using Databricks and Spark. Implement CI/CD workflows, maintain high-quality code, and oversee data pipelines' evolution for cross-functional teams.
Top Skills:
AWSCi/CdDatabricksDelta LakeDockerGitlabKubernetesPythonScalaSparkTerraform
Artificial Intelligence • Cloud • Computer Vision • Hardware • Internet of Things • Software
As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.
Top Skills:
DatabricksDbtPythonRdsRedshiftSnowflakeSparkSQL
_0.png)
AdTech • Marketing Tech
Lead large-scale technical initiatives on the Data Ingestion Team, focusing on building and operating distributed systems that process billions of events daily.
Top Skills:
AerospikeGoKafkaKubernetesTidbVitess
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
