Team Lead, Data Engineering

Overview

Haptiq is a leader in AI-powered enterprise operations, delivering digital solutions and consulting services that drive value and transform businesses. We specialize in using advanced technology to streamline operations, improve efficiency, and unlock new revenue opportunities, particularly within the private capital markets. 

Our integrated ecosystem includes PaaS - Platform as a Service, the Core Platform, an AI-native enterprise operations foundation built to optimize workflows, surface insights, and accelerate value creation across portfolios; SaaS - Software as a Service, a cloud platform delivering unmatched performance, intelligence, and execution at scale; and S&C - Solutions and Consulting Suite, modular technology playbooks designed to manage, grow, and optimize company performance. With over a decade of experience supporting high-growth companies and private equity-backed platforms, Haptiq brings deep domain expertise and a proven ability to turn technology into a strategic advantage. 

The Opportunity

This position is for a Team Lead, Data Engineering with deep expertise in cloud data architecture, data modeling, SQL, and data governance for enterprise-level systems.

Responsibilities and Duties

* Design, develop, and maintain enterprise data architecture strategies, standards, and blueprints that support operational, analytical, and AI/ML workloads.

* Architect cloud-native data solutions across AWS (Redshift, RDS, Glue, Lake Formation) or equivalent platforms, ensuring scalability, security, and cost efficiency.

* Define and enforce data modeling standards, including dimensional modeling, denormalized schemas, OLTP/OLAP design patterns, and AI-friendly ontologies.

* Architect and oversee data transformation layers using DBT, ensuring modular, tested, and well-documented models across the analytics and reporting stack.

* Lead the design of data integration and orchestration patterns using Prefect and Airflow, including batch ETL, real-time streaming, event-driven architectures, and API-based data exchange.

* Define and implement data validation, quality control, and testing frameworks to ensure accuracy, completeness, and consistency of data across pipelines and warehouses.

* Establish data quality SLAs, monitoring, and alerting standards; design automated checks and reconciliation processes to catch issues before they impact downstream consumers.

* Establish and maintain data governance frameworks covering data quality, lineage, cataloging, classification, and access control.

* Collaborate with Data Engineers, Software Engineers, Product, and Analytics teams to translate business requirements into scalable, maintainable data designs.

* Evaluate and recommend data technologies, tools, and platforms; own the technical decision-making for data infrastructure within assigned domains.

* Design data partitioning, indexing, and optimization strategies to support high-performance queries and big data workloads.

* Define and document data contracts, schemas, and interface specifications across services and teams.

* Ensure data architectures are designed to support downstream AI/ML consumption, including feature stores, embedding pipelines, and model training datasets where applicable.

* Perform architecture reviews and code reviews to ensure adherence to data standards, optimal execution patterns, and long-term maintainability.

* Validate and cleanse data and handle error conditions gracefully.

* Mentor data engineers on best practices in data modeling, architecture patterns, and cloud data design.

* Assist with automated release management and CI/CD processes as they relate to data infrastructure and pipeline deployments.

Requirements

* Experience managing and mentoring a team is required - proven experience managing, mentoring, and developing team members.

* 7+ years of experience in data architecture, data engineering, or related technical roles.

* 5+ years of experience designing and implementing cloud-based data architectures (AWS, GCP, or Azure).

* 5+ years writing complex SQL queries with RDBMSes.

* 5+ years of experience with developing and deploying ETL/ELT pipelines using Airflow, Prefect, or similar tools.

* Strong experience with DBT for data transformation, testing, and documentation.

* Experience with data warehouse design: OLTP, OLAP, star schemas, snowflake schemas, dimensions, and facts.

* Experience with data modeling tools and methodologies (conceptual, logical, physical models).

* Experience with cloud-based data warehouses such as Redshift, Snowflake, or BigQuery.

* Experience implementing data validation frameworks, quality control processes, and automated testing for data pipelines.

* Familiarity with how data architectures serve AI/ML workloads, including feature stores and vector-based retrieval patterns.

* Strong understanding of data governance, data quality frameworks, and metadata management.

* Experience with cloud-based data architectures, messaging, and analytics.

* Bachelor's degree in Computer Science or equivalent - preferred

Pluses - Experience with

* Python development, including Pandas and PySpark

* Docker

* Kubernetes

* CI/CD automation

* AWS Lambdas/Step Functions

* Data partitioning

* Databricks

* Vector databases (Pinecone, Weaviate, pgvector)

* Data mesh or data fabric architectural patterns

* Graph databases or knowledge graph design

* Cloud certifications

Why Join Us?

We value creative problem solvers who learn fast, work well in an open and diverse environment, and enjoy pushing the bar for success ever higher.  We do work hard, but we also choose to have fun while doing it. 

The annual compensation range for this role is $135,000 – $145,000 CAD