About the Role
What you’ll do:
You’ll be embedded directly with enterprise customers - understanding their systems, deploying our platform, and owning outcomes end-to-end. You’ll work at the intersection of data integrity, enterprise architecture, and applied AI.
You’ll build and deploy by:
+ Embedding with customer engineering and data teams to map their full system landscape - SAP, Snowflake, Databricks, Postgres, APIs - and pinpoint exactly where data breaks
+ Deploying and configuring the Shamrock ai validation platform across customer environments, integrating with their existing infrastructure
+ Translating customer business rules into validation logic: schemas, consistency checks, cross-system reconciliation rules, and real-time alerting pipelines
+ Building data connectors, transformation layers, and custom pipelines that feed our validation infrastructure at enterprise scale
+ Ensuring AI-driven customer workflows are gated on validated, consistent data - working with their ML teams to instrument pre-inference checks and feedback loops
You’ll also:
Interface directly with customer stakeholders - from data engineers and architects to CIOs - to align on scope, progress, and outcomes
Feed real-world failure patterns, edge cases, and feature gaps back to our product and engineering teams
Contribute to architecture and design decisions alongside fellow engineers, shaping how the platform evolves.
You’ll own problems end-to-end: discovery → integration → deployment → iteration.
Why You’ll Love Working at Shamrock:
+ Impactful Work: You will be solving critical data integrity problems at Fortune 500 companies in healthcare and financial services - industries where data failures have real consequences.
+ Cutting-Edge Technology: Get hands-on experience with the latest in AI and LLM technology.
+ World-Class Team: Work alongside and learn from a team of seasoned entrepreneurs and industry experts.
+ Early-Stage Opportunity: Join a fast-growing startup and have a significant impact on our product and culture.
Requirements
Engineering background in Computer Science, Software Engineering, or Data Science Proficiency in Python and/or TypeScript/JavaScript; comfort picking up new languages as needed
Solid understanding of data systems: relational databases (SQL), data warehouses (Snowflake, BigQuery, Redshift), and data pipeline concepts
Experience building or consuming REST API integrations between systems
Ability to read and reason about data schemas, identify anomalies, and design validation logic for complex real-world datasets
Strong communication skills - equally comfortable with a data engineer debugging a schema and a CIO asking about business risk
7+ years of experience in software engineering, data engineering, or a closely related technical role
Willingness to travel to customer sites as needed (~15–30%)
Nice to Have (But, not required):
Hands-on experience with ERP systems (SAP, Oracle, Microsoft Dynamics)
Familiarity with Databricks, dbt, Apache Spark, or similar data platform tooling
Experience with data quality frameworks or schema validation tools (e.g., Great Expectations, dbt tests)
Prior solutions engineering, technical consulting, or forward deployment experience
Exposure to AI/ML pipelines and understanding of how upstream data quality affects model behavior
About the Company
We’re building systems that continuously validate data and business processes across large enterprise environments. Enterprises run on multiple systems: ERP (e.g., SAP), APIs, internal tools, and data platforms (Databricks, Snowflake, Postgres). Inconsistencies in data - either from external vendors, internal processes, or data migrations break workflows. When AI is layered on top, those failures scale.
We build the layer that:
+ Prevents inconsistent data entry
+ Detects inconsistencies across systems
+ Validates business logic in real time
+ Enables AI-driven workflows to run safely and reliably
We’re already live at a Fortune 100 AI company and launching at Fortune 500 scale companies in healthcare and financial services.