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Mecka AI is building the data infrastructure layer for robotics and embodied AI. The company designs and operates global systems for data capture, labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
As Data Delivery Lead, you own the final mile of Mecka's business: transforming raw captured, labeled, and validated data into exact customer deliverables. This is a hands-on, data-native role bridging internal data systems and customer specifications.
Key responsibilities include:
**Deliverable Assembly & Export**: Pull final delivery cuts from internal data systems (document and columnar databases, file stores), format data to customer schemas, generate manifests and indexes, and stage handoffs to customer buckets and cloud storage reliably.
**Delivery QA & Validation**: Run every deliverable through Mecka's ship-gates before customer delivery. Design and execute validation including coverage stats, stratified sample audits, schema compliance, and quality-drift detection. Set internal acceptance bars higher than customer QC standards.
**Spec Translation**: Convert customer data specifications into concrete, testable gates and queries. Translate requirements back to capture, labeling, and operations teams to ensure specs are met.
**Customer-Facing Artifacts**: Produce delivery reports, data catalogs, sample packs, and schema documentation. Maintain canonical reference packs and make data quality legible to technical customers.
**Throughput & Delivery Management**: Track delivery performance against timelines and SLAs. Identify and eliminate bottlenecks in the assembly-to-ship path. Scale delivery processes as volume and customer count grow.
**Cross-Functional Execution**: Partner with data capture, labeling operations, and engineering teams. Own delivery communication internally and externally. Keep timelines, risks, and status transparent.
You bring 1–3 years in data analytics, data engineering, or analytics engineering roles. You're fluent in SQL and comfortable querying and reshaping large, imperfect datasets independently. You have working proficiency with Python or similar scripting languages for data manipulation, validation, and automation. You understand data pipelines, schemas, and data-quality concepts, with proven ability to own data workflows end-to-end. Strong communication and stakeholder-management skills are essential.
Ideal candidates have experience in fast-paced startups, exposure to ML/AI training data or dataset delivery, familiarity with NoSQL/document stores and columnar databases (ClickHouse, BigQuery, Snowflake), and experience building QA/validation tooling.
