- 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 fast-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 adaptable 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

📌 Data Delivery Lead (Toronto)
🏢 Jobtailor
📍 Toronto