Pull final delivery cuts from internal data systems, including document and columnar databases and file stores Format data to customer schemas and generate manifests, indexes, and packaging Stage and reliably and reproducibly hand off deliveries to customer buckets and cloud storage Own delivery mechanics end-to-end Run every deliverable through internal ship-gates Design and execute validation including coverage statistics, stratified sample audits, schema compliance, and quality-drift detection Set an internal acceptance bar higher than the customer's QC Build and maintain golden reference sets and repeatable quality checks Translate customer data specifications into concrete, testable gates and queries Translate customer requirements into capture, labeling, and computer-vision operations requirements Serve as technical interpreter between customer requirements and internal execution Produce delivery reports, data catalogs, sample packs, and schema documentation Maintain a current canonical reference pack Track delivery performance against timelines and SLAs Identify and remove bottlenecks in the assembly-to-ship process Scale the delivery process as volume and customer count grow Partner with data capture, labeling operations, and engineering to close specification gaps Own delivery communication with internal teams and external customers Keep timelines, risks, and deliverable status transparent Requirements 1–3 years in data analytics, data engineering, analytics engineering, or a similarly data-heavy role Fluent in SQL; comfortable querying and reshaping large, imperfect datasets independently Working proficiency with a scripting language (Python preferred) for data manipulation, validation, and automation Solid grasp of data pipelines, schemas,



and data-quality concepts Proven ability to own a data workflow or deliverable end-to-end Excellent communication and stakeholder-management abilities Experience in quick-paced startups or high-growth environments Exposure to ML / AI training data, data labeling, or dataset delivery Experience with NoSQL / document stores and/or columnar analytics databases Experience with ClickHouse, BigQuery, or Snowflake Built QA / validation tooling or data-quality checks Strong intuition for prioritization and tradeoffs between speed, quality, and cost Rigorous and detail-obsessed Structured in thinking but flexible in execution Comfortable operating in ambiguity and fast-changing environments Data-native; instinct to measure, not assume Calm under pressure and able to run multiple deliveries in parallel without dropping quality Core Competencies Demonstrates expertise in data analytics and engineering, with a strong focus on SQL, data quality, and validation processes. Capable of managing end-to-end data workflows while ensuring high standards of communication and stakeholder management. Highest-signal resume keywords SQL Proficiency Data Pipeline Management Data Quality Assurance Python Scripting for Data Manipulation Experience with ClickHouse, BigQuery, or Snowflake ATS Optimization Keywords Hard Skills Data Analytics Data Engineering Data Quality Concepts Data Manipulation Data Validation Data Schemas Data Workflows Data Delivery Data Cataloging Data Performance Tracking Soft Skills Excellent Communication Stakeholder Management Detail-Oriented Structured Thinking Calm Under Pressure Industry Keywords Data Capture Data Labeling ML / AI Training Data High-Growth Environments Fast-Paced Startups Tools & Technologies SQL Python NoSQL Databases Document Stores Columnar Analytics Databases ClickHouse BigQuery Snowflake #J-18808-Ljbffr

📌 Data Delivery Lead (Toronto)
🏢 Jobtailor
📍 Toronto