Senior Data Engineer - Remote
$110,000–$150,000 year
RemoteUnited States
Job Summary
Design, build, and maintain scalable ETL/ELT pipelines for batch and near-real-time data processing using Python, SQL, Snowflake, and distributed technologies. Integrate data from diverse sources into the enterprise platform, develop complex transformations, and implement reusable frameworks to improve developer productivity. Ensure pipeline reliability through automated testing, CI/CD, Infrastructure as Code, and robust monitoring and observability capabilities. Provide technical leadership by mentoring engineers, participating in architecture decisions, and collaborating with Data Science and Product teams to support AI/ML workloads. Troubleshoot production issues, optimize performance and cost, and enforce data quality and security standards. Work remotely within the U.S., with occasional travel to offices as needed.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related technical field
- 5+ years of experience building production-grade data pipelines, data platforms, and large-scale data processing solutions
- Advanced proficiency in Python and SQL with strong software engineering fundamentals
- Hands-on experience with Snowflake and cloud-based data platforms
- Experience with Kafka, Flink, or other distributed and streaming technologies
- Strong experience with AWS, including services such as S3, Lambda, Glue, Kinesis, or equivalent cloud technologies
- Experience designing and optimizing ETL/ELT pipelines, data models, and high-volume data processing workflows
- Experience with orchestration technologies such as Airflow or equivalent platforms
- Experience with CI/CD, Git, automated testing, and Infrastructure as Code
- Strong understanding of data quality, governance, security, observability, and production support
- Strong problem-solving, communication, and cross-functional collaboration skills
- A 'full-stack mindset', not hesitating to do what it takes to solve a problem end-to-end
Desired Qualifications
- Experience transforming data leveraging dbt, preferably dbt cloud
- Experience working in AI-native engineering environments and effectively leveraging AI-assisted development tools to improve engineering velocity, code quality, and operational efficiency
- Experience supporting Machine Learning and Generative AI workloads, including feature engineering and ML data pipelines
- Experience with Docker, Kubernetes, Terraform, or CloudFormation
- Experience with data cataloging, lineage, metadata management, and data observability platforms
- Experience in Healthcare, HealthTech, SaaS, or other regulated industries
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