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 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

Quick-Paced Startups

Tools & Technologies

SQL

Python

NoSQL Databases

Document Stores

Columnar Analytics Databases

ClickHouse

BigQuery

Snowflake

📌 Data Delivery Lead (Toronto)
🏢 Jobtailor
📍 Toronto