Senior AI Reconciliation Analyst

Dave is a financial app on a mission to build products that level the financial playing field. It is redefining the financial landscape by leveraging technology to create an affordable, transparent, and user-centric access to liquidity for millions of Americans. As a leading innovator in the U.S. financial services sector, Dave’s digital financial platform offers products designed to meet the credit needs of those underserved by traditional financial institutions. Dave’s offerings include its flagship ExtraCash product, providing members up to $500 in short-term advances within minutes. The company is on track to launch several new product offerings in 2026, including a Buy Now Pay Later (BNPL) option.

Dave is focused on serving Americans who are financially vulnerable or living paycheck to paycheck. Dave is leading the charge in creating a new era of credit products that prioritizes speed, affordability, and accessibility, making it the go-to financial partner for those who need it most.

At Dave, data is the foundation of the financial infrastructure we build for millions of members. As our first Senior AI Reconciliation Analyst, you'll establish the reconciliation foundation for Dave's data systems — designing the pipelines, anomaly rules, and AI-powered matching logic that keep our financial data trustworthy and audit-ready. You'll sit at the intersection of data engineering, finance, and operations, serving as the technical lead who helps Engineering, Finance/Accounting, and Reconciliation Operations move faster and catch problems before they become incidents.

This role is for someone who thinks in systems, is energized by financial data complexity, and wants to build something foundational from the ground up.

• Build the reconciliation foundation. Design and implement the data infrastructure that reconciles transactions across internal systems (e.g.,operation, ledger) and external partners (ACH networks, banking rails, payment processors), ensuring every dollar in equals every dollar out.

• Lead anomaly detection and rules development. Develop and maintain a library of anomaly detection rules — covering timing differences, partial matches, unexpected delays, and true errors — and evolve them over time using ML-based signals as data matures.

• Implement AI-powered matching. Apply probabilistic and fuzzy matching techniques to automatically resolve ambiguous or near-match transactions, reducing manual review queues and improving straight-through processing rates.

• Flag and triage unmatched records. Build real-time and batch pipelines that surface unmatched, delayed, or anomalous transactions to the right stakeholders, with enough context for fast resolution.

• Partner cross-functionally as the technical lead. Serve as the primary data partner for Engineering (system integration and pipeline reliability), Finance/Accounting (period-end close, audit support), and Recon Operations (workflow tooling, exception handling).

• Define data quality standards. Establish validation frameworks that distinguish expected timing lags from genuine discrepancies, and document data contracts between upstream systems.

• Drive automation and process improvement. Identify and eliminate manual reconciliation workloads through scalable pipelines, alerting systems, and self-serve tooling for operations teams.

• Elevate the team. Share reconciliation and data quality best practices, mentor analysts, and help establish this function as a core capability within the Analytics org.

• 5+ years of experience in data analytics, data engineering, or financial data roles — ideally with direct exposure to reconciliation, settlements, or financial operations.

• Deep SQL proficiency and strong Python skills (pandas, PySpark, or equivalent); experience with DBT a plus.

• Hands-on experience with Snowflake and/or Databricks, including pipeline development, scheduling, and data modeling.

• Familiarity with ACH transaction cycles, payment rails, or financial settlement processes — you understand why timing differences exist and how to model around them.

• Experience building anomaly detection logic, whether rule-based, statistical, or ML-driven.

• Exposure to probabilistic/fuzzy matching techniques for entity resolution or transaction matching.

• Strong cross-functional communication skills — you can translate a data quality issue into clear language for a Finance partner and a technical spec for an Engineer.

• Practical AI/ML knowledge, including supervised and unsupervised methods relevant to reconciliation — anomaly detection (isolation forest, autoencoders), clustering for transaction grouping, and classification models for match/no-match decisions.

• Experience working with LLMs or AI-assisted tooling (e.g., prompt engineering, LLM-based data extraction, or AI-augmented workflow automation) to accelerate analysis and reduce manual review.

• Experience in fintech or financial services strongly preferred.

• Bachelor's degree in a field requiring analytical or structured thinking (e.g., STEM, Economics, Finance); Master's degree preferred.

• You're building from zero. This is a greenfield role — you'll define what best-in-class reconciliation infrastructure looks like at Dave, and your decisions will shape how the company manages financial data for years.

• Your work has direct financial impact. Every unmatched transaction you catch, every anomaly rule you write, and every manual process you automate has a measurable effect on financial accuracy and operational efficiency.

• You'll sit at the center of the business. Reconciliation touches everything — member disbursements, ExtraCash collections, banking partner settlements, and period-end close. You'll have visibility into the full financial picture.

• You'll solve genuinely hard problems. Timing lags, partial matches, multi-rail money movement — financial reconciliation is one of the most intellectually demanding data challenges in fintech.

• You'll contribute to a bigger mission. Accurate, trustworthy financial data underpins every product decision we make. Your work directly supports Dave's mission to level the financial playing field for everyday people.

SQL, Python, Snowflake, dbt, Airflow, Looker

Location: Remote — open to US

Don’t let imposter syndrome get in the way of an incredible opportunity. We’re looking for people who can help us achieve our mission and vision, not just check off the boxes. If you’re excited about this role, we encourage you to apply. You may just be the right candidate for this or other roles.

Why you’ll love working here: 

At Dave, our people are just as important as our product. Our culture reflects the values that guide who we are, how we work, and what we aspire to be. Daves are member-centric, helpful, transparent, persistent, and better together. We strive to create an environment where all Daves feel valued, heard, and empowered to do their best work. As a virtual-first company, team members can live and work anywhere in the United States, except Hawaii. 

A few of our benefits & perks:

💚 Opportunity to tackle tough challenges, learn and grow from fellow top talent, and help millions of people reach their personal financial goals

💻 Flexible hours and virtual-first work culture with a home office stipend

🏥 Premium Medical, Dental, and Vision Insurance plans

👶 Generous paid parental and caregiver leave

💰 401(k) savings plan with matching contributions

📈 Financial advisor and financial wellness support

🏖️ Flexible PTO and generous company holidays, including Juneteenth and Winter Break

🎉 All-company in-person events once or twice a year and virtual events throughout to connect with your team members and leadership team

Dave Operating LLC is proud to be an Equal Employment Opportunity employer and is dedicated to cultivating a diverse and inclusive workplace. We will consider for employment all qualified applicants and do not discriminate on any basis protected by federal, state, or local law, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance relating to an applicant's criminal history.

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