<div><b>Best Egg</b><span> is the leading financial confidence platform that provides flexible solutions to real-life challenges for people with limited savings. We leverage real-time customer insights and data science to connect more people with the right products for their financial needs. Our offers include a growing suite of products such as personal loans, a credit card, and flexible rent, which are complemented by a suite of financial health tools to help customers make smart financial decisions and stay on track, so they can be money confident no matter what life throws at them.</span></div><div><span> </span></div><div><span>Our culture and values inspire our employees and customers to embrace Best Egg. We are committed to championing a culture of inclusiveness and diversity of thought, and we focus on providing a safe, flexible, and collaborative work environment. Our employees are encouraged to engage in creative problem solving, and we promote opportunities for growth and enrichment across the organization. </span></div><div><span> </span></div><div><span>If you are inspired by inspiring others, Best Egg is the place for you.</span></div><div><br /></div><div><span>As a member of the ML Feature Platform Engineering team, you will play a key role in building and scaling the core data and feature engineering pipelines that power machine learning and AI at Best Egg. Our platform enables data scientists to quickly prototype, train, and deploy models with consistent, reliable, and high-quality features across batch, streaming, and real-time contexts. The platform leverages a modern tech stack including Python, SQL, Polars, Narwhals, Snowflake, AWS, Kubernetes, and FastAPI. Your work will directly impact the productivity of data scientists, the robustness of ML models, and the ability to innovate with new features in production.</span></div><h3>Key Responsibilities</h3><ul><li>Build Robust Data Pipelines: Design and implement SQL- and Python-based pipelines (using dataframes such as Polars/Narwhals) that support both backfills for training and low-latency, real-time serving.</li><li>Feature Engineering: Collaborate with data scientists to design, build, and maintain new features from complex time series data sources, ensuring they are reusable, well-documented, and consistent across environments.</li><li>Data Debugging &amp; Support: Help data scientists troubleshoot complex SQL queries, debug feature outputs, and optimize queries for performance.</li><li>Platform Reliability: Quickly diagnose and resolve platform issues spanning Python services (FastAPI), Snowflake queries, Kubernetes services, and real-time pipelines.</li><li>Developer Experience: Deliver high-quality abstractions, tools, and libraries that simplify feature development and improve data scientist workflows.</li><li>Performance &amp; Scalability: Monitor, profile, and optimize data pipelines and feature services for throughput, latency, and cost efficiency.</li></ul><h3>What Youll Need to Succeed</h3><ul><li>Professional Experience: 5+ years in data engineering, backend engineering, ML engineering, or related software development roles with a proven track record of building and maintaining large-scale data systems.</li><li>Strong Data Engineering Expertise: Advanced SQL skills, experience with Python dataframes (e.g., Polars, pandas, Narwhals), and a deep understanding of data modeling and feature engineering best practices.</li><li>Hands-On Experience with Cloud Warehouses: Significant experience with Snowflake, BigQuery, Redshift, or Databricks in production settings.</li><li>Pipeline Development Skills: Ability to design Python data pipelines (DAGs) that work seamlessly across batch and real-time contexts, including concepts like incremental processing and backfills.</li><li>Backend Engineering Experience (Secondary but Valuable): Solid Python development skills (concurrency, async, API design) and experience with FastAPI (or similar) for building data services.</li><li>Debugging &amp; Troubleshooting: Comfort in quickly investigating complex issues across SQL, Python, and infrastructure layers.</li><li>DevOps &amp; Deployment Knowledge: Working experience with Docker, Kubernetes, CI/CD workflows, and infrastructure-as-code (Terraform/CloudFormation).</li><li>Collaboration &amp; Mentorship: Ability to partner effectively with data scientists, analysts, and engineers while promoting best practices in feature engineering and data platform design.</li></ul><div><b>Best Egg celebrates diversity and equal opportunity.</b><span> We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better we will grow.</span></div><div><br /></div><div><b>Employee Benefits</b></div><div><span>Best Egg offers many additional benefits for our employees, including (but not limited to):</span></div><div><span>·       Pre-tax and post-tax retirement savings plans with a competitive company matching</span></div><div><span>         program</span></div><div><span>·       Generous paid time-off plans including vacation, personal/sick time, paid short--      </span></div><div><span>        term and long-term disability leaves, paid parental leave, and paid company  </span></div><div><span>        holidays </span></div><div><span>·       Multiple health care plans to choose from, including dental and vision options</span></div><div><span>·       Flexible Spending Plans for Health Care, Dependent Care, and Health    </span></div><div><span>        Reimbursement Accounts</span></div><div><span>·       Company-paid benefits such as life insurance, wellness platforms, employee</span></div><div><span>        assistance programs, and Health Advocate programs</span></div><div><span>·       Other great discounted benefits include identity theft protection, pet insurance,</span></div><div><span>         fitness center reimbursements, and many more!</span></div>

Best Egg

Senior Software Engineer, ML Data

Best Egg

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