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 rapid-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

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📌 Data Delivery Lead (Ontario)
🏢 Jobtailor
📍 Ontario