Job Duties and ResponsibilitiesCandidates must be willing to participate in at least one in-person interview, which may include a live whiteboarding or technical assessment session.You will address the challenge of transforming complex, fragmented data requirements into scalable, production-ready AI infrastructure using advanced Databricks and Spark architectures. By implementing rigorous software engineering disciplines and automated CI/CD workflows, you will eliminate bottlenecks in data delivery and ensure the reliability of mission-critical pipelines. Your role is pivotal in harmonizing cross-functional goals with high-quality code standards to drive the next generation of our data platform's evolution.
What Success Looks Like (Objectives)- Deliver scalable, high-performance data pipelines using Databricks and Spark that meet rigorous departmental OKRs for performance and cost-efficiency
- Build fully automated CI/CD workflows within Gitlab to reduce deployment friction and ensure 100% version-controlled data infrastructure
- Apply SOLID engineering principles and modular design to create a reusable testing framework that guarantees data pipeline stability and quality
- Foster a culture of excellence by leading technical code reviews and mentoring peers to elevate the team's overall software engineering maturity
- Integrate AI-driven automation tools to proactively monitor pipeline health and optimize resource allocation across the AWS ecosystem
- Facilitate seamless collaboration between Data Science and Product teams to transform experimental models into fault-tolerant production solutions
Skills, Experience and RequirementsCore Skills and Competencies (What you'll bring)- Advanced proficiency in Python and SQL alongside a deep understanding of distributed systems architecture and modern data patterns
- Expertise in Databricks, Spark, and Delta Lake orchestration to manage large-scale, high-velocity data environments
- A strong foundation in DevOps methodologies, specifically regarding infrastructure-as-code and containerization using Docker or Kubernetes
- AI Application literacy, with the ability to leverage machine learning libraries and NLP frameworks to enhance data processing capabilities
- Proven capability in applying design patterns and testing frameworks to ensure the integrity of complex software ecosystems
- Critical experience in building and managing highly available, fault-tolerant systems within an enterprise AWS environment
- Background in handling sensitive data and maintaining strict security protocols
Minimum Requirements- Minimum Education: Bachelor's Degree in Computer Science, Electrical Engineering, or a related field
- Minimum Experience: 3+ years of experience in data engineering
- Required Technical Skills: Must have at least 3+ years of experience with:
- Python and SQL
- AI Platforms - Databricks and Spark (including Delta Lake)
- CI/CD pipelines and Gitlab workflows
Visa sponsorship not available for this role
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Salary RangesCompensation: $100,980.00/Year - $136,625.00/Year
BenefitsWe 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.
