• Ingest, clean, transform, and structure customer and internally generated engineering data for AI training and inference. • Design and build high-quality mechanical components and assemblies in CAD to serve as authoritative ground truth for evaluating and training AI systems. • Produce labeled datasets, reference designs, annotations, exploded views, sequences, and other engineering artifacts that encode real-world reasoning. • Apply engineering judgment to define and assess output quality across datasets. • Collaborate with Product Managers to shape tooling used for annotation, data correction, model-output review, and pipeline automation. • Partner closely with engineering and research teams to understand model data requirements, failure modes, and areas needing new data. • Work with customers to understand their data sources, schemas, formats, and quality expectations.

• Own the definition and evaluation of output quality, ensuring consistency and accountability across all internal and external datasets. • Oversee ingestion, cleaning, transformation, and structuring of customer data for AI model training and inference. • Build scalable processes and frameworks for data labeling, annotation tools, quality checks, and feedback loops. • Ensure timely, high quality data delivery for internal R&D and customer deployments. • Own SLAs, accuracy targets, and “definition of quality” for all datasets. • Partner with Product Managers to shape data-related features, workflows, and tooling (e.g., annotation UI, correction UI, model output review tools). • Translate customer needs into clear data requirements and repeatable solutions. • Build and lead a Data Studio team. • Implement best practices for documentation, quality metrics, repeatable workflows, and compliance. • Drive continuous improvements in throughput, cost efficiency, and accuracy. • Work closely with engineering and research teams to define data needs for model training, evaluation, and debugging. • Collaborate with BD team to scope data requirements for onboarding and implementation. • Serve as the subject matter expert on data quality, data formats, labeling standards, and customer specific nuances related to manufacturing design. • Interface with customers to understand their domain, data structures, and quality expectations. • Guide customers through data preparation, schema definition, and ongoing iteration. • Own delivery timelines, reporting, and communication for all active data projects.

• Design, develop, and maintain data ingestion pipelines using Kafka Connect and Debezium for real-time and batch data integration. • Ingest data from MySQL and PostgreSQL databases into AWS S3, Google Cloud Storage (GCS), and BigQuery. • Implement best practices for data modeling, schema evolution, and efficient partitioning in the Bronze Layer. • Ensure reliability, scalability, and monitoring of Kafka Connect clusters and connectors. • Collaborate with cross-functional teams to understand source systems and downstream data requirements. • Optimize data ingestion processes for performance and cost efficiency. • Contribute to automation and deployment scripts using Python and cloud-native tools. • Stay updated with emerging data lake technologies such as Apache Hudi or Apache Iceberg.

• Architect, build, and maintain next-generation data pipelines • Design and build robust, scalable ELT pipelines to ingest data into Snowflake • Own the dbt project structure, developing complex SQL-based data models • Manage the Snowflake environment for cost-efficiency and performance • Champion data integrity and implement observability tools • Mentor junior engineers and establish best practices for SQL and version control