Senior Data Platform Engineer

Jellyvision

Senior Data Platform Engineer at Jellyvision building and operating data processing pipelines across platforms. Working in a hands-on role on a high-ownership data team with various responsibilities.

Posted 5/7/2026full-timeRemote • California, Colorado, Florida, Illinois, Kentucky, Minnesota, Missouri, New York, North Carolina, Ohio, Oregon, Pennsylvania, South Carolina, Tennessee, Texas, Utah, Virginia, Washington, Wisconsin • 🇺🇸 United StatesSenior💰 $127,000 - $156,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowApacheAWSCloudPythonSQLTerraform

About the role

Key responsibilities & impact

As a Senior Data Platform Engineer, you’ll be a hands-on engineer on a small, high-ownership data team.
You’ll work across the full data platform - relational, warehouse, and lakehouse systems - building and operating the pipelines that power compliance, analytics, and reporting workloads.
Design and build pipelines that move data across systems - supporting data lake ingestion, compliance workloads, and cross-domain data flows.
Own pipeline operations end to end: monitoring, incident resolution, data quality, and documentation that lets any team member respond independently.
Identify technical debt and reliability risks and raise them with clear context and proposed next steps.
Design and maintain schemas across relational, warehouse, and lakehouse layers, working with application engineers and product to get data models right.
Build out the platform’s service layer, infrastructure-as-code, and data quality frameworks - this role spans design and implementation.
Keep platform documentation at a level where any team member can understand what exists, how it works, and where the risks are.
Over time, contribute to the analytics engineering layer, including modeling practices and semantic layer development.
Contribute to evaluations of the current platform against emerging architectures and tooling, helping produce trade-off analyses and recommendations.
Bring what you see day to day in the systems you operate into the team’s improvement roadmap and technical direction.
Track and report on platform health metrics: pipeline uptime, failure rates, data freshness, and cost trends.
Mentor peers and junior engineers through code review, pairing, and technical guidance.

Requirements

What you’ll need

7–9+ years of data engineering or data platform experience with hands-on ownership of production systems
Experience building and operating a data lakehouse, data lake, or modern warehouse architecture (Snowflake, Databricks, or comparable)
Deep fluency with Apache Airflow or comparable orchestration: DAG design, task dependencies, sensors, and production operations
Solid understanding of open table formats (Iceberg, Delta, Hudi) and columnar storage (Parquet, Avro, ORC), including how format choices affect query performance, storage efficiency, and schema evolution
Strong Python: production-grade code, testing, packaging, and debugging
Advanced SQL: complex transformations, performance tuning, and debugging against a cloud warehouse
Hands-on relational schema design, ideally in a multi-tenant SaaS context
Terraform or comparable IaC for managing cloud data resources; CI/CD for pipeline or infrastructure deployment
Familiarity with AWS data infrastructure: S3, IAM, and relevant managed services
Experience using AI-assisted development tools (Claude Code, Cursor, Copilot, or similar) to accelerate engineering workflows
Demonstrated ownership of systems you’ve inherited and systems you’ve built from scratch - you can assess an unfamiliar codebase and improve it, and you’re just as effective designing something new
Clear written communication: you can describe a system’s state, a problem, or a recommendation in plain language
Experience mentoring other engineers through code review, pairing, or technical guidance.

Benefits

Comp & perks

Check out our benefits here!

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringdata platform experiencedata lakehouse architecturedata lake architecturemodern warehouse architectureApache AirflowPythonSQLTerraformrelational schema design

Soft Skills

clear written communicationmentoringtechnical guidanceincident resolutiondata qualityownershipcollaborationproblem-solvingdocumentationevaluating systems