Auditoria.AI · 2 months ago
Senior Data Engineer, Agentic AI Applications
Santa Clara, CA (3 days hybrid in office)
Full-time
Hybrid
Senior Level
5+ years expAuditoria.AI is an AI-driven SaaS automation provider for corporate finance, focused on automating back-office processes and enhancing financial operations. As a Senior Data Engineer, you will be responsible for building and optimizing the data infrastructure for their SmartResearch platform and SmartBots, ensuring data quality and performance while collaborating with data scientists and AI engineers.
Artificial Intelligence (AI)FinanceFinTechMachine LearningSaaSSoftware
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
Qualification
Required
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
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
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
Company
Auditoria.AI
Auditoria is a saas provider of ai-driven cognitive automation to help companies accelerate cash performance and transform the back office.
51-200 employees
H1B Sponsorship
Auditoria.AI has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (1)
2024 (1)
2022 (1)
2021 (1)
Funding
Current Stage
Growth StageTotal Funding
$59.5MKey Investors
Innovius CapitalKPMGVenrock
2025-02-24Series B· $38M
2023-09-27Series Unknown
2021-03-31Series A· $15.5M
Leadership Team
Rohit Gupta
Founder and CEO
Adina Simu
Chief Product Officer, Chief Commercial Officer, Co-Founder
Recent News
Company data provided by crunchbase