Senior Business Analyst at Ocrolus Inc.

Job description

Come build at the intersection of AI and fintech. At Ocrolus, we’re on a mission to help lenders automate workflows with confidence—streamlining how financial institutions evaluate borrowers and enabling faster, more accurate lending decisions. Our AI workflow and analytics platform for lenders is trusted at scale, processing nearly one million credit applications every month across small business, mortgage, and consumer lending. By integrating state-of-the-art open- and closed-source AI models with our human-in-the-loop verification engine, Ocrolus captures data from financial documents with over 99% accuracy. Thanks to our advanced fraud detection and comprehensive cash flow and income analytics, our customers achieve greater efficiency in risk management, and provide expanded access to credit—ultimately creating a more inclusive financial system. Trusted by more than 400 customers—including industry leaders like Better Mortgage, Brex, Enova, Nova Credit, PayPal, Plaid, SoFi, and Square—Ocrolus stands at the forefront of AI innovation in fintech. Join us, and help redefine how the world’s most innovative lenders do business. Key Responsibilities Design and implement data architecture and data models that optimize data processes and improve efficiency. Build and maintain reliable, scalable data pipelines that keep data available and trustworthy in Snowflake or another database or data warehouse. Write and optimize SQL queries to extract, manipulate, and analyze large datasets for business insights. Use Python to integrate, transform, and automate data workflows. Analyze data to find patterns and root causes, and present findings with clear visualizations (Tableau, Sigma, or similar) that support data-driven decisions. Collaborate with cross-functional teams to gather business requirements and translate them into technical specifications. Optimize and streamline existing analytics processes to improve reporting capabilities. Manage relationships with key stakeholders, understand their data needs, and deliver timely, actionable insights. Required Skills Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Information Systems, or a related field. Experience: 4+ years in data analytics, data architecture, and process optimization. Analytical strength: Strong analytical and problem-solving skills, with the ability to break down ambiguous business questions, find patterns and root causes in large datasets, and back conclusions with data and Visualization. SQL: Expert at writing complex queries, tuning query and database performance, and managing large datasets. Architecture and modeling: Designed scalable data models and platforms (dimensional, star/snowflake schemas) that handle high data volumes, with sound pipeline management. Python: Strong working knowledge for data integration, manipulation, and automation. Stakeholder management: Communicates clearly with both technical and non-technical teams. Preferred (Nice to Have) Experience with Snowflake and ELT/orchestration tools (dbt, Airflow), plus Git/CI-CD for analytics code. Data quality, observability, and governance practices, such as validation checks, lineage, and SLAs. Snowflake cost and performance tuning (warehouse sizing, clustering, query profiling). AI-driven analytics: hands-on use of LLMs and AI tools (for example Snowflake Cortex or Claude) for text-to-SQL, automated insights, or anomaly detection. Life at Ocrolus We’re a team of builders, thinkers, and problem solvers who care deeply about our mission — and each other. As a fast-growing, remote-first company, we offer an environment where you can grow your skills, take ownership of your work, and make a meaningful impact. Our culture is grounded in four core values: Empathy – Understand and serve with compassion Curiosity – Explore new ideas and question the status quo Humility – Listen, be grounded, and remain open-minded Ownership – Love what you do, work hard, and deliver excellence We believe diverse perspectives drive better outcomes. That’s why we’re committed to fostering an inclusive workplace where everyone has a seat at the table, regardless of race, gender, gender identity, age, disability, national origin, or any other protected characteristic. We look forward to building the future of lending together.

Requirements

Must-haves

Design and implement data architecture and data models that optimize data processes and improve efficiency.
Build and maintain reliable, scalable data pipelines that keep data available and trustworthy in Snowflake or another database or data warehouse.
Write and optimize SQL queries to extract, manipulate, and analyze large datasets for business insights.
Use Python to integrate, transform, and automate data workflows.
Analyze data to find patterns and root causes, and present findings with clear visualizations (Tableau, Sigma, or similar) that support data-driven decisions.
Collaborate with cross-functional teams to gather business requirements and translate them into technical specifications.
Optimize and streamline existing analytics processes to improve reporting capabilities.
Manage relationships with key stakeholders, understand their data needs, and deliver timely, actionable insights.
Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Information Systems, or a related field.
Experience: 4+ years in data analytics, data architecture, and process optimization.
SQL: Expert at writing complex queries, tuning query and database performance, and managing large datasets.
Architecture and modeling: Designed scalable data models and platforms (dimensional, star/snowflake schemas) that handle high data volumes, with sound pipeline management.
Python: Strong working knowledge for data integration, manipulation, and automation.
Stakeholder management: Communicates clearly with both technical and non-technical teams.
Experience with Snowflake and ELT/orchestration tools (dbt, Airflow), plus Git/CI-CD for analytics code.
Data quality, observability, and governance practices, such as validation checks, lineage, and SLAs.
Snowflake cost and performance tuning (warehouse sizing, clustering, query profiling).
AI-driven analytics: hands-on use of LLMs and AI tools (for example Snowflake Cortex or Claude) for text-to-SQL, automated insights, or anomaly detection.

Nice-to-haves

Preferred (Nice to Have)

Application prep

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Keywords to mirror

aisqlpython

Platform: greenhouse
Effort: quick