Data Engineer

ParentSquare

5d•$130,000 - $160,000•Remote

About The Position

We're looking for a Senior Data Engineer to join our team in building and evolving our data platform - from scalable pipelines and data warehouse architecture to governance and quality frameworks baked into the design. You'll contribute to and champion our data lakehouse strategy, shape how we think about data products, and help set the standard for modern data engineering practices across the organization. You'll partner closely with Product Engineering, DevOps, and InfoSec to deliver on our architectural vision, and work hand in hand with our Data Scientists to ensure the right data is available, modeled, and ready to power meaningful analysis and discovery. This role is for a solutions evangelist - someone who is genuinely energized by where data platforms are going.

Requirements

5+ years of experience in data engineering, analytics or a closely related field
Strong SQL and Python skills; comfortable working across the full data stack
Hands-on experience with modern data tooling and platforms - Fivetran, dbt, Snowflake, Databricks or Redshift.
Experience building data pipelines on cloud platforms (AWS preferred), and infrastructure-as-code tooling such as Terraform
Experience with change data capture and event-driven data patterns
Solid understanding of modern warehouse and lakehouse architecture, data modeling fundamentals including Kimball dimensional modeling and medallion design patterns
Experience with data quality frameworks, and governance practices
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

Nice To Haves

Experience with AI-driven development tools (Cursor, Copilot, or similar) integrated into day-to-day engineering workflows is a bonus!

Responsibilities

Design and Scale Data Infrastructure: Architect and maintain scalable, reliable data pipelines and systems across our stack.
Drive Data Quality and Governance: Build frameworks and standards that ensure data is accurate, well-documented, and trustworthy at every layer.
Evolve Our Platform: Continuously evaluate and improve our solutions - optimizing for performance, cost, and scalability while bringing best practices and a forward-thinking approach to every decision.
Enable the Business: Partner with Data Science, Analytics, and business teams to develop pipelines, data models, metric definitions and semantic layers and ensure the data is consistent and accurate.
Balance Speed and Scale: Recognize when to move fast with a pragmatic solution and when to invest in building the right system - and communicate those tradeoffs clearly to stakeholders.
Champion Modern Practices: Embrace AI-assisted development workflows, stay current on tooling, and help the team continuously raise the bar.