Overview
Senior Data Engineer responsible for building scalable data platforms, pipelines, distributed processing workflows, and AI-enabled data engineering tools.
What you'll do
- Develop and maintain end-to-end data pipelines and backend ingestion workflows.
- Build and operate large-scale Spark and PySpark batch and streaming workflows.
- Optimize Spark jobs through partitioning, shuffle, caching, and resource allocation.
- Design data architecture, transformations, monitoring, observability, and data quality practices.
- Build MCP servers and integrate AI-agent workflows into data engineering.
- Lead projects and collaborate with cross-functional stakeholders on scalable data solutions.
- Mentor junior team members and provide technical guidance and training.
What you'll need
- Bachelor's degree in computer science, data engineering, data science, information technology, or an equivalent engineering program.
- 8+ years of software engineering with a data focus or data engineering experience.
- 5+ years building and maintaining production-grade data pipelines, including data modeling.
- 5+ years of production experience with Spark or PySpark, including performance tuning.
- Strong Python and SQL programming skills.
- Experience with cloud data warehouses or lakehouses and AWS, Azure, and/or GCP.
- Experience with APIs, relational databases, and ETL tools such as Fivetran or dbt.
Nice to have
- Experience designing and governing a centralized semantic layer.
- Logging and monitoring experience with Splunk, Datadog, or AWS CloudWatch.
- Experience with AWS Serverless services including API Gateway, Lambda, S3, SNS, SQS, and Secrets Manager.
Details
- Remote position open to candidates residing in the United States, excluding the San Francisco Bay, New York City, and Washington, D.C. metro areas.
- Full-time role.
- Annual base salary: $118,107.50–$178,650 USD.
Read the full description and apply on the company’s own careers page.
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