Top Data Engineer Jobs in Ottawa

Build and operate production data pipelines and dimensional data models using Spark and SparkSQL. Own pipelines from requirements through deployment, monitoring, and iteration; improve data quality, lineage, reliability, and cost efficiency. Partner with data scientists, analysts, product managers, and engineers to support datamarts, KPIs, reporting, and analysis. Participate in a business-hours on-call rotation and contribute to runbooks and alerting.

Develops, operates, troubleshoots, and modernizes enterprise data integrations using Talend. Responsibilities include building ETL/ELT pipelines, APIs, batch and real-time jobs, SQL transformations, scheduling, monitoring, production support, incident resolution, data quality, root-cause analysis, and platform maintenance. The role collaborates with technical and business stakeholders across development, testing, and production environments, while applying AI and automation to improve pipeline development, testing, monitoring, and documentation.

Build and operate platform infrastructure for product analytics, data processing, and ML systems. Responsibilities include event tracking pipelines, Databricks Bronze-to-Gold lakehouse layers, schema evolution, data contracts, CI validation, backfills, monitoring, feature computation, model pipelines, lineage, PHI policy enforcement, and self-service developer tooling. The role focuses on making data and ML changes safe, observable, reproducible, and accessible to engineers and subject-matter experts.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Design and build scalable backend systems, APIs, data models, event-driven services, experimentation platforms, and personalization systems. Partner with Product, Data Science, Analytics, and Design teams to support customer acquisition and engagement. Establish secure, observable, high-performance engineering practices, mentor junior engineers, and participate in monitoring, on-call, and operational support.

Build reusable Python libraries and reproducible analysis pipelines for image and experimental data; define data models and provenance; partner with wet‑lab scientists to translate requirements and validate methods; establish testing, validation, versioning, and observability for scientific workflows.

Design, build, and maintain SparkSQL/PySpark data pipelines in the central data lake to ingest IoT, product, and unstructured data (video/audio). Produce reliable computed tables for analytics, model training, and dashboards; integrate external datasets; ensure high data quality and uptime; and collaborate with Data Science, ML, and cross-functional teams.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, workflow automation, evaluation, and observability capabilities. Work hands-on with Python, SQL, dbt, lakehouse data, cloud infrastructure, model selection, and inference-cost optimization. Establish reusable engineering patterns and mentor teams through high-quality code and system design.

Build and operate scalable data pipelines supporting batch and streaming workloads, AI and ML systems, analytics, and sports intelligence products. Responsibilities include ETL/ELT development, data modeling, orchestration, retrieval pipelines, data quality, lineage, monitoring, infrastructure optimization, and collaboration with ML, product, and analytics teams.

Drive technical sales and customer adoption of Microsoft Azure cloud database and analytics solutions through demos, proofs of concept, hackathons, and architecture workshops. Advise customers on secure, scalable data-platform architectures, migrations, and AI applications. Build relationships with technical decision-makers, resolve implementation blockers, collaborate with engineering teams, and maintain expertise across Microsoft Fabric, Azure Databases, Databricks, Purview, and competing data platforms.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Own Orum’s data platform architecture, roadmap, reliability, and scalability. Build batch and real-time ETL/ELT pipelines using PostgreSQL, event streams, ClickHouse, BigQuery, and dbt. Administer Looker and develop LookML models, dashboards, and a trusted semantic layer. Establish data quality, governance, lineage, observability, and metric standards while partnering with Product, Finance, GTM, and Operations on analytics, AI, and business intelligence initiatives.

Build and operate Block’s data platform, including warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop reliable Python services, developer tools, and Terraform infrastructure for petabyte-scale data. Partner with data practitioners and product teams, establish engineering standards, provide technical direction, and mentor engineers while ensuring security, reliability, observability, usability, and data governance.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

Build and operate high-volume telemetry and analytics data pipelines supporting real-time monitoring, product decisions, experimentation, and user experience analysis. Design event instrumentation, schemas, quality checks, deduplication, late-event handling, and identity stitching. Partner with data science, product, and SRE teams to shape requirements, and integrate AI tooling into development and data quality workflows.

Build and support enterprise data lakes, lakehouses, ETL/ELT pipelines, streaming platforms, and business intelligence solutions. Responsibilities include processing large-scale data, implementing data quality, governance, metadata, lineage, monitoring, anomaly detection, and observability frameworks, and supporting production Linux-based distributed data platforms. The role also provides technical leadership, mentorship, code and design reviews, performance optimization, and support for secure handling of sensitive information.

Design and operate scalable, cloud-native data pipelines and orchestration systems for imaging, biomarker, and healthcare data. Build Databricks and distributed processing workflows, integrate DICOM data into data lakes, and implement validation, observability, monitoring, and fault tolerance. Collaborate with data science, AI, R&D, product, and data management teams to deliver secure, compliant, reliable data for analytics and machine learning.

Build and operate Ground News’s data platform, including warehouse integrations, scalable dimensional models, analytical layers, and KPI infrastructure. Own ingestion pipelines, data quality, monitoring, incident response, infrastructure as code, and deployment workflows across AWS and GCP. Partner with analysts, ML engineers, and product stakeholders to create reliable, reusable data models and ensure accurate, observable, cost-effective data systems.

Designs, develops, tests, and maintains cloud-based data pipelines and warehouses for large-scale datasets. The role involves data ingestion using Python and SQL, GCP services, production migrations, infrastructure troubleshooting, and optimization. The engineer collaborates with stakeholders to understand project goals and applies AI/LLM tools thoughtfully within data engineering workflows. Responsibilities also include distributed data manipulation, technical guidance, and maintaining scalable, cost-efficient data architecture.

Designs, builds, and operates enterprise data lakes, warehouses, lakehouses, ETL/ELT pipelines, databases, and reporting platforms. Prepares governed, model-ready data for AI and machine learning, supports feature stores and embedding workflows, administers relational databases, optimizes queries and dashboards, and ensures data quality, security, availability, and disaster recovery. Collaborates with technical and business teams, reports to senior management, and mentors data engineers.

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Lead event-driven and batch processing decisions, establish standards for observability, freshness, and failure handling, and guide cross-functional platform design. Mentor engineers, conduct design reviews, influence teams without direct authority, and drive foundational platform improvements. The role requires deep Python and SQL expertise, cloud data warehouse experience, scalable API-based ETL design, orchestration, and containerized infrastructure experience.