• Act as the technical bridge between a signed contract and a successful launch • Map out customer technical onboarding journeys • Own the end-to-end data migration process • Provide integration support using API and system integrations • Translate customer problems into technical requirements

• Build, maintain, and optimize scalable data pipelines and workspaces within the Databricks environment. • Develop and audit complex reporting models focused on advertising performance metrics and financial billing. • Write high-performance SQL queries and leverage Jinja SQL to create dynamic and reusable transformations. • Design data models that support accurate and actionable business insights. • Collaborate with cross-functional teams to ensure data reliability and reporting accuracy. • Explore and implement basic AI/ML enhancements such as predictive billing models or anomaly detection. • Support automation efforts through CI/CD practices using GitHub and Jenkins. • Maintain clean and well-documented configuration files using YAML/YML. • Continuously improve data workflows for efficiency, scalability, and quality.

• 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. • Continuously refine standards for metadata, annotation, and model quality, maintaining a living “definition of quality” for ME datasets. • Collaborate with Product Managers to shape tooling used for annotation, data correction, model-output review, and pipeline automation. • Provide detailed feedback on tool usability, workflow efficiency, and automation opportunities. • Help develop scalable, repeatable data processes that improve throughput and data consistency. • Partner closely with engineering and research teams to understand model data requirements, failure modes, and areas needing new data. • Influence model behavior by supplying representative engineering examples and ground-truth mechanical designs. • Partner with customer-facing teams to translate domain requirements, industry standards, and customer data schemas into actionable dataset specifications. • Serve as a subject matter expert on mechanical engineering formats, CAD standards, manufacturing practices, and design artifacts. • Generate technical documentation, exploded views, sequences, and annotations that encode engineering reasoning into training data. • Ensure that datasets reflect real-world constraints, DFM (Design for Manufacturing) considerations, material behavior, and industry best practices. • Embed engineering reasoning into training data so that AI systems learn not just geometry or text, but engineering intent. • Work with customers to understand their data sources, schemas, formats, and quality expectations. • Guide customers in preparing high-quality datasets, defining structured schemas, and improving data pipelines. • Support delivery timelines by communicating progress clearly and surfacing risks or issues early. • Review and work with external contractors, ensuring high-quality output and adherence to SOPs.

• Engineer distributed ingestion services that reliably pull data from diverse sources, handle messy real-world edge cases, and deliver clean, well-structured outputs to multiple downstream products. • Build high-throughput processing components (batch and/or near-real-time) with a focus on performance, scalability, and predictable cost, using strong profiling and measurement practices. • Design and evolve data contracts (schemas, validation rules, versioning, backward compatibility) so downstream teams can build with confidence. • Own production quality: write maintainable code, strong unit/integration tests, and add the observability you need (metrics/logs/tracing) to diagnose issues quickly. • Improve platform reliability by hardening pipelines against partial failures, retries, rate limits, data drift, and infrastructure issues—then codify those learnings into better tooling and guardrails. • Contribute to CI/CD and developer experience: faster builds, better test signal, safer releases, and automated operational checks. • Participate in design reviews, code reviews, incident retrospectives, and iterative delivery—making pragmatic trade-offs and documenting them clearly.