We’re an AI-driven SaaS automation provider for corporate finance that automates back-office business processes involving tasks, analytics, and responses in Accounts Payable and Accounts Receivable. By leveraging natural language processing, artificial intelligence, and machine learning, based on proprietary small language models and commercial LLMs, Auditoria removes friction and repetition from mundane tasks while automating complex functions and providing real-time visibility into cash performance. Corporate finance and accounting teams use Auditoria to accelerate business value while minimizing heavy IT involvement, improving business resilience, lowering attrition, and accelerating business insights.

Founded in 2019 and backed by KPMG, Workday Ventures, Dell Technologies, Venrock, Innovius Capital, Sentinel Global, Neotribe Ventures, Engineering Capital, and Firebolt Ventures, we build AI TeamMates that drive intelligent automation by combining fine-grained analytical orchestration of a company’s typical financial and audit workflows with conversational AI, delivering rapid value to the finance/audit back office.

We’ve received numerous awards including:

CBInsights Fintech 100 – 2025
Global Excellence Awards – Best AI-Driven Finance Automation Platform – 2025 Winner
Q3 2024 Constellation ShortList™ for AI-Driven Cognitive Applications for the fifth year in a row.
The Gartner Emerging Tech Impact Radar: Artificial Intelligence Report 2024 named Auditoria as a sample vendor for Composite AI.
Named a sample vendor for intelligent applications in finance in the Hype Cycle for Autonomous Accounting, 2024, the Hype Cycle for Finance AI and Advanced Analytics, 2024, and the Hype Cycle for the Future of Enterprise Applications, 2024 reports.

About the Role

We are scaling an AI/ML-enabled Enterprise SaaS solution that powers agentic automation for corporate finance operations across multiple Fortune 500 companies. As a Senior Data Engineer, you’ll be instrumental in building and optimizing the data infrastructure that powers our next-generation SmartResearch platform and SmartBots, enabling real-time financial intelligence and autonomous decision-making.

You’ll own critical components of our data architecture during this high-growth phase, working with modern data stack technologies including, but not limited to, MySQL/Postgres, Snowflake, MongoDB, and implementing medallion architecture patterns (bronze-silver-gold) to support both operational systems and AI/ML workloads. This role is critical to ensuring data quality, performance, and governance as we scale our agentic AI capabilities.

Key Responsibilities

Data Pipeline Architecture & Development: designing, building, and maintaining the systems and workflows that move, transform, and process data from various sources to destinations where it can be used for analysis, AI applications, or business operations.
Database & Data Warehouse Optimization: designing efficient data storage structures and improving query performance to ensure fast, reliable access to data for analytical and operational needs.
Enterprise Integration & Data Sourcing: connecting to and extracting data from various business systems (like ERP platforms) to make that data available for use across the organization.
Data Quality & Observability: implementing systems that monitor, validate, and ensure the accuracy and reliability of data while providing visibility into how data pipelines are performing.
Multi-Tenant Architecture & Security: designing data systems that safely separate and protect multiple customers’ data within shared infrastructure while ensuring compliance and access controls.
AI/ML Support & Collaboration: working with data scientists and AI engineers to prepare, transform, and deliver data in formats optimized for training and running machine learning models and AI applications.
Documentation & Best Practices: creating clear technical documentation and establishing standardized approaches for data engineering work across the organization.

Qualifications

Bachelor’s degree in Computer Science, Data Engineering, or related field.
5+ years of professional experience in data engineering or related roles with a proven track record of building production-grade data pipelines and data infrastructure at scale.
Strong proficiency in SQL with deep expertise in query optimization, indexing strategies, and performance tuning across relational databases (MySQL, PostgreSQL, Aurora).
Hands-on experience with Snowflake and Databricks including data modeling, query optimization, dynamic tables, streams, tasks, and performance tuning for analytical workloads.
Extensive experience with NoSQL databases such as MongoDB or AWS DocumentDB, including schema design, aggregation pipelines, and materialized views.
Expert-level knowledge of ETL/ELT processes and experience with modern data orchestration tools (Apache Airflow, Prefect) and streaming technologies (Kafka, Debezium, AWS Kinesis).
Strong understanding of data warehousing concepts, medallion architecture (bronze-silver-gold), and dimensional modeling principles.
Proven experience with AWS data services including RDS/Aurora, S3, Kinesis, Lambda, and CloudWatch.
Proficiency in Python for data engineering tasks including data transformation, pipeline orchestration, and automation scripts.
Experience implementing data quality frameworks, monitoring, and observability solutions for production data systems.
Strong understanding of data modeling principles for both operational (OLTP) and analytical (OLAP) systems.
Experience with version control systems (Git), CI/CD pipelines, and infrastructure as code practices.

Preferred Qualifications

Experience integrating with ERP systems such as Workday, Oracle Fusion or SAP.
Hands-on experience with vector databases (Pinecone, Weaviate, ChromaDB, or others) and building data pipelines for AI/ML applications and RAG systems.
Graph databases (Neo4j, Amazon Neptune) for complex relationship modeling.
Knowledge of big data technologies (Spark, Hadoop, Hive) and data lake architectures for large-scale data processing.
Streaming data architectures and event-driven systems for real-time data processing and low-latency use cases.
Deep understanding of multi-tenant architecture patterns, data isolation strategies, and implementing RBAC/SOD controls in data platforms.
Familiarity with data governance frameworks, data cataloging (AWS Glue Data Catalog, Alation), and data privacy regulations (GDPR, SOC2).
Containerization and orchestration technologies (Docker, Kubernetes) for deploying data pipeline workloads.
Building data infrastructure for AI/ML workloads, including feature stores, data versioning, and experiment tracking.

Benefits & Perks

Competitive startup compensation package
Early-stage equity options
Comprehensive health benefits
Unlimited PTO
Flexible work environment
Opportunity to shape the future of AI in enterprise finance
Collaborative, innovation-driven culture

Auditoria.AI is an equal opportunity employer committed to building a diverse and inclusive team. We welcome candidates of all backgrounds passionate about combining cutting-edge AI with practical business solutions.

Please no third party recruitment agencies/vendors at this time, thank you.

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