Senior Data Engineer, FutureFit

• Important note: Data shows that men typically apply when meeting 3/10 requirements, while women often wait until it's 10/10. We encourage you to apply if you see a strong (not necessarily perfect) fit
• Proven ability to translate ambiguous business problems into clear, actionable insights
• Hands-on experience using SQL and Python for analysis in a professional setting
• Experience building and maintaining data pipelines, warehouses, and infrastructure
• Strong communication skills to convey technical insights to both technical and non-technical stakeholders
• Demonstrated ownership of analytics solutions, ensuring accuracy, reliability, and business alignment
• Familiarity with data visualization tools such as Looker, Power BI, or Tableau
• Familiarity with modeling structured and unstructured data, including NoSQL databases like MongoDB
• A sharp, kind, and open-minded approach, driven by both excellence and impact
• Your alma mater isn’t our focus. Your grit, hunger, and drive are. If you learn continuously, tackle challenges head-on, and know your strengths and gaps intimately—you’re our person

Desirable

• We’re seeking a Sr. Data Engineer to join our team. You will play a key role in evolving our core data platform which includes data pipelines, machine learning models, and various databases
• Combine technical data expertise with strong business intuition to build a foundation of reliable data. Your expertise in data engineering will help us build strong, performant pipelines
• Our data infrastructure: Building and maintaining the infrastructure that powers our data platform including pipeline orchestration, data warehousing, and machine learning
• Data solutions that drive the product: Develop and maintain data solutions alongside a team of data scientists that enable our product to function at scale and with quality
• Data governance and quality: Uphold best practices in data governance, ensuring accuracy, accessibility, and compliance across data systems
• Cross-platform data sourcing: Surface and integrate data from across the platform to address real business needs in product, engineering, and GTM
• Evolving core data models: Continuously evolve our foundational models by identifying and incorporating new, high-value data sources
• 30 days:
• Onboarding/Learning Stack/Product
• Learn who are customers are, what their problems are, and how we can leverage data to support them
• Gain an understanding of our core data entities and how they drive the product
• Contribute to our core data pipelines by adding of data quality and data enrichment layers
• 60 days:
• Work with data scientists to develop datasets and processes that streamline complex workflows
• Contribute to and own aspects of our data catalog by defining and maintaining metrics, dimensions, and lineage
• Support surrounding teams in getting value out of our platform’s data through regular reporting and analysis
• 90 days:
• Own and automate reporting workflows from data ingestion all the way to building out dashboards and tools
• Independently gather reporting and insights requirements from stakeholders
• Present findings to stakeholders and provide recommendations to drive us towards making data-driven decisions

• Each journey includes a mix of interviews and a performance challenge. For this role, that might look like:
• Online Application
• Initial Screen with Recruiting Partner
• Interview with Hiring Manager
• Performance Challenge
• Final 1:1 Interviews
• Final Decision
• Generally, this entire process takes around 6 weeks, although the timing can vary due to specific candidate circumstances