Lead the design and implementation of data pipelines that prepare high-quality training data for AI models.
Build data curation workflows that transform raw enterprise data into labeled, validated datasets.
Design data quality frameworks: validation, profiling, anomaly detection, lineage tracking.
Extend existing anonymized data export pipelines to support AI training workloads.
Implement synthetic data generation pipelines.
Design schema mappings across 197+ enterprise tables for feature extraction.
Collaborate with ML engineers on training data format requirements.
Establish data catalog and metadata management for AI training artifacts.

10+ years of software engineering experience, with 5+ years in data engineering.
Strong experience with Apache Spark / PySpark and large-scale data processing.
Experience building ETL/ELT pipelines on cloud infrastructure (managed Spark, object storage, managed ETL, or equivalent).
Knowledge of data quality frameworks and data governance.
Experience with data anonymization and privacy-preserving data processing.
Understanding of ML training data requirements.
Proficiency in Python and SQL.
Experience with data catalog tools and metadata management.
BS/MS in Computer Science or equivalent experience.
Experience in B2B SaaS with multi-tenant data preferred.

Senior Lead AI Engineer, Data

Key skills