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UpsideUpsideWashington, DC

Principal Data Engineer

Principal Data Engineer leading platform modernization, infrastructure, and data product development for a high-impact analytics engineering team. Owns architecture, migrations, and cross-functional initiatives using Snowflake, dbt, Dagster, and AWS.

215k – 250k
Remote8+ YOEData Engineering

About the role

Responsibilities

  • Lead platform modernization: deprecate legacy workflows, migrate pipelines to scalable patterns, and improve CI/CD and developer experience
  • Drive infrastructure and FinOps initiatives across Snowflake, Dagster, and dbt to reduce cost, improve governance, and increase maintainability
  • Own platform evolution projects such as making data consumable by agentic tools and improving orchestration across the analytics stack
  • Design and deliver complex, domain-critical data products used by analysts, data scientists, and product teams
  • Architect reusable, extensible patterns for modeling, orchestration, and transformation
  • Lead technical planning across cross-functional teams, breaking down large data initiatives into scoped workstreams
  • Mentor engineers, build internal documentation and tooling, and raise standards for analytics engineering
  • Partner with engineering, product, data science, and business stakeholders to deliver end-to-end solutions
  • Represent Data Engineering in design forums and shape the future roadmap for data

Requirements

  • 8+ years in data or analytics engineering with a track record of owning complex, business-critical systems end to end
  • Hands-on experience with AWS (deployments, workload management, cloud infrastructure) and the modern data stack (Snowflake, dbt, Dagster, Databricks, Terraform)
  • Track record of leading platform migrations, deprecations, or upgrades across shared systems
  • Ability to design secure, reusable patterns for data ingestion, access control, and platform automation
  • Experience with DevOps practices (CI/CD for data), data governance, or FinOps
  • Ability to break down ambiguous, cross-functional problems and lead implementation from design to deployment
  • Clear communication across engineers, analysts, product managers, and business leaders
  • Drive to mentor others, set standards, and improve systems

Preferred Qualifications

  • Experience supporting ML workflows (building features or monitoring model inputs and outputs)
  • Background in a fast-growing startup or on platform-style teams that serve internal customers

Skills

AWSSnowflakedbtDagsterDatabricksTerraformCI/CDData GovernanceFinopsData Modeling

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