Senior Data Engineer

iQmetrix•Winnipeg, MB

50d•Hybrid

About The Position

The Senior Data Engineer will design, build, and improve the data platform capabilities that power analytics, embedded reporting, operational data products, and AI-ready datasets. This role goes beyond pipeline implementation. It includes data modeling, platform design, performance tuning, governance, observability, and the creation of reusable engineering patterns that support scalable and trustworthy data products. The ideal candidate is a hands-on engineer with strong production experience in Python, SQL, and distributed compute environments. They should have deep familiarity with modern lakehouse patterns and layered data product design, and should be comfortable providing technical leadership through mentoring, design reviews, code reviews, and platform stewardship.

Requirements

5+ years of experience in data engineering, software engineering, analytics engineering, or a closely related field.
Strong proficiency in SQL and Python, with production experience in distributed compute environments and a solid understanding of execution models, partitioning, and optimization.
Hands-on experience with cloud-native lakehouse platforms and modern data lake storage patterns, including Delta Lake or equivalent.
Strong opinions about layered data product design - specifically separation of concerns between raw, refined, and curated data - and experience enforcing those boundaries at scale in a governed environment.
Experience designing, building, and maintaining production ETL/ELT pipelines for analytical or operational workloads.
Strong understanding of data modeling concepts, including dimensional modeling, curated data products, and semantic-ready data structures.
Familiarity with asset-based or software-defined orchestration approaches, version control, CI/CD practices, and production support for data systems.
Strong understanding of data quality, observability, governance, lineage, and secure data access patterns.
Ability to communicate technical trade-offs clearly and partner effectively across engineering, product, analytics, and business teams.
Experience mentoring other engineers through code reviews, design reviews, troubleshooting, and shared engineering standards.

Nice To Haves

Experience with Unity Catalog or equivalent metadata and governance layers in a cloud data platform.
Experience with event-driven, streaming, or near-real-time data patterns in cloud or lakehouse ecosystems.
Experience building data products that directly support predictive model development - including feature preparation, label definition, and pipelines that feed model training and evaluation workflows.
Experience supporting generative AI or agent workflows through structured and unstructured data preparation, retrieval patterns, or evaluation datasets.
Experience working in a SaaS product organization with multiple products, domains, tenants, or customer-specific data boundaries.
Familiarity with cost optimization practices for cloud data platforms.

Responsibilities

Design, build, and optimize scalable data pipelines and curated data products using Python, SQL, and distributed compute - with a strong understanding of execution models, partitioning, and performance tuning.
Develop and maintain data models across raw, refined, and curated layers to support reporting, embedded analytics, operational workflows, machine learning, and emerging AI use cases.
Build reliable, reusable, and well-documented data assets consumed by analytics, product, engineering, and downstream platform teams.
Design data structures that support multi-tenant SaaS reporting, dimensional modeling, semantic analytics, and governed access patterns across multiple products and customer boundaries.
Own orchestration using asset-based or software-defined orchestration patterns - where pipelines are modeled as versioned, observable data assets with clear ownership and dependency contracts, not just task graphs.
Improve the performance, reliability, observability, and cost efficiency of data processing workflows across the platform.
Implement and advance data quality, lineage, governance, and secure access control practices using modern lakehouse tooling and platform standards.
Partner with product, software engineering, analytics, and AI stakeholders to translate business workflows into reliable data products and platform capabilities.
Contribute to platform architecture decisions, reusable engineering patterns, data onboarding standards, and the ongoing evolution of the organization's data platform strategy.
Support event-oriented and near-real-time data patterns where needed to enable downstream operational and product use cases.
Troubleshoot complex data issues, lead root-cause analysis, and improve the resilience of pipelines, jobs, and platform services.
Operate comfortably within containerized or cloud-native platform infrastructure, including understanding how data services interact with surrounding platform components.
Mentor junior and intermediate engineers, review code and designs, and help establish best practices for data engineering, analytics enablement, and AI/ML-supporting data workflows.