Data Engineer (AI & Data Enablement)

We are seeking a Senior Enterprise Data Engineer – AI & Data Enablement to support enterprise-scale data sourcing, engineering, quality, and enablement initiatives. The ideal candidate will come from a strong Data Engineering background and have recently expanded into AI/ML or GenAI projects.

This role is primarily focused on enterprise data engineering and sourcing, ensuring high-quality, reliable, and accessible data is available to support analytics, AI, and business initiatives.

Key Responsibilities:

• Source, integrate, transform, and prepare data from complex enterprise data environments.
• Develop and maintain scalable data pipelines, ETL/ELT processes, and data workflows.
• Work extensively with Snowflake, Databricks, SQL, and SQL-based data warehouses.
• Analyze enterprise data requirements and identify appropriate source systems, datasets, and data elements.
• Enable data for downstream analytics, AI/ML, reporting, and business applications.
• Investigate data quality issues, including discrepancies, missing data, duplicates, inconsistent values, mapping issues, and transformation errors.
• Perform root-cause analysis and develop sustainable solutions to data quality and pipeline issues.
• Validate data accuracy, completeness, consistency, and reliability across source and target systems.
• Collaborate with data engineers, data scientists, AI teams, analysts, and business stakeholders to ensure data is fit for purpose.
• Document data sources, transformations, business rules, data definitions, and quality requirements.
• Support emerging AI/GenAI initiatives by ensuring enterprise data is properly sourced, structured, validated, and enabled.
• Contribute to continuous improvements in enterprise data architecture, data quality, and data enablement practices.

Required Qualifications:

• Strong professional background in Data Engineering.
• Hands-on experience with Snowflake.
• Hands-on experience with Databricks.
• Strong SQL skills and experience working with SQL-based data warehouses.
• Experience with enterprise-scale data sourcing, integration, transformation, and data pipelines.
• Strong understanding of data quality, data validation, and troubleshooting.
• Demonstrated ability to investigate data discrepancies and identify root causes.
• Experience working with large, complex enterprise datasets.
• Strong understanding of data enablement and making data accessible and usable for downstream consumers.
• Ability to work effectively with technical and business stakeholders.

Preferred Qualifications:

• Experience with Palantir.
• Experience supporting AI/ML, Generative AI, or other AI initiatives.
• Experience preparing enterprise data for AI/ML applications.
• Knowledge of modern data architecture, data governance, and data management practices.
• Experience with cloud data platforms and enterprise data ecosystems.

Ideal Candidate Profile:

The ideal candidate is a Data Engineer first, with strong hands-on experience in enterprise data sourcing and engineering, who has recently moved into or supported AI/ML and GenAI projects. You should be comfortable working deep in the data while understanding how high-quality enterprise data enables modern AI applications.