About Our Client
Our client is reshaping the consumer finance landscape by bringing a more human approach to the industry. Their data-powered products help financial institutions modernize their collections operations, giving borrowers clear, compassionate paths back to financial stability and control. Beyond expanding access to credit, the company is focused on restoring dignity and offering millions of people a genuine opportunity to achieve financial freedom.
About the Role
As our client’s founding Senior Data Engineer, you’ll redefine how the company uses data to broaden access to credit 鈥� not by patching what already exists, but by unlocking what’s still possible. You’ll take complete ownership of the modern data stack, evolving it from a capable system maintained part-time by analysts and engineers into a best-in-class platform that anticipates and supports the company’s most ambitious data initiatives. You’ll design the data infrastructure that helps millions of people regain financial footing, ensuring every insight moves seamlessly from production systems to the decision-makers who rely on it. By establishing data engineering as a core discipline at the company, you’ll free analysts to focus on insight generation while you build the scalable foundation that powers the next stage of growth.
Key Responsibilities
猞佲�僌wn and optimize the entire data platform 鈥� evolving the Snowflake warehouse from analyst-maintained to engineer-optimized while standardizing data models for client reporting, operational dashboards, and ML features.
猞佲�傿uild self-healing data pipelines 鈥� designing ETL processes that scale automatically with volume, implementing monitoring that surfaces issues before anyone notices, and tuning cost without compromising performance.
猞佲�僁emocratize data access 鈥� designing intuitive models that empower PMs, analysts, and ops teams to find answers on their own, all while upholding security and compliance standards.
猞佲�傿ridge engineering and analytics 鈥� creating feedback loops between production systems and analytical needs, making sure schema changes don’t disrupt downstream dependencies, and influencing how new features generate data.
猞佲�僆nstitute modern data practices 鈥� rolling out testing frameworks, building CI/CD pipelines for infrastructure changes, and producing documentation that allows others to extend your work.
猞佲�僁rive strategic infrastructure decisions 鈥� pinpointing where new tools unlock capabilities, balancing quick wins against long-term architectural vision, and laying the groundwork for an eventual data engineering team.
猞佲�僁eliver immediate impact through key projects, including:
Priority Projects
猞佲�僁ata Model Redesign: Architect unified models that cut query redundancy for client reporting by 50% while preserving flexibility.
猞佲�働ipeline Reliability: Reinforce monitoring systems to catch 99% of issues before they reach users.
猞佲�僀ost Optimization: Reduce Snowflake spend by 30鈥�40% through smart clustering and lifecycle management.
猞佲�傾nalytics Enablement: Build semantic layers that let both technical and non-technical users easily draw value from rich user data.
Requirements
猞佲��5+ years in data engineering or analytics engineering with steadily growing technical scope (data or analytics engineering should be the primary discipline in your most recent role).
猞佲�僁eep expertise with modern data warehouses (Snowflake, BigQuery, or Redshift), including performance tuning and cost optimization.
猞佲�傾dvanced SQL skills 鈥� you can write clean, elegant queries and figure out why that 45-minute monster is burning through the compute budget.
猞佲�働roduction experience with dbt or comparable transformation tools, including testing and documentation best practices.
猞佲�僁emonstrated ability to build and maintain ETL/ELT pipelines at scale using modern orchestration tools.
猞佲�僂xperience as a sole or lead data engineer, owning infrastructure end-to-end without a large team behind you.
猞佲�僂xperience implementing data quality frameworks and proactive monitoring systems.
Bonus Skills
猞佲�僂xperience with streaming architectures and real-time analytics.
猞佲�僃amiliarity with ML infrastructure and feature stores.
猞佲�僈nowledge of financial data privacy regulations and compliance.
猞佲�働revious startup or high-growth company experience.
猞佲�傾 track record of partnering with engineering teams to improve data quality at the source.
猞佲�傾 systems thinker who looks past individual pipelines to understand how data flows across the organization.
猞佲�僌wnership mentality 鈥� you set your own roadmap and move initiatives forward without waiting for permission.
猞佲�僑trategic perspective that ties technical decisions back to business outcomes.
猞佲�僀ollaborative working style with analysts, engineers, and product managers.
猞佲�僀lear communicator who writes documentation people actually read.
猞佲�傿ias toward shipping iteratively rather than chasing perfection.
Logistics
Location: New York
Compensation: $170K 鈥� $190K + Equity
Openings: 1
Benefits / Other: Open to relocation for strong non-NYC candidates (relocation required within 60 days); visa transfers considered by default (new visa sponsorships handled case by case).
Interview Process
1.鈥僐ecruiter Screen
2.鈥僅iring Manager Screen
3.鈥僀ase Study / Panel
4.鈥僌nsite Interviews
5.鈥僀ulture / CEO Interview
6.鈥僌蹿蹿别谤