Sr. Data Engineer

A large client of ours is looking for a Sr. Data Engineer, details are as follows:

Engagement: Contract, with potential for permanent conversion based on fit.
Term: 6 months, with renewals in June and December for further 6-month terms.
Location: Winnipeg or Toronto — hybrid, 3 days per week in office.
Rate: commensurate with experience.

Responsibilities:

• Lead the design, build, test, deployment, and maintenance of end-to-end data pipelines (ingestion, transformation, integration) using SAP HANA, SAP Data Services, and Python, orchestrated and scheduled through Stonebranch.
• Own delivery outcomes for critical data pipelines, including operational ownership of production systems running under defined service levels.
• Partner with business and technical stakeholders to identify data opportunities, prioritize initiatives, assess feasibility, and maximize the value of data delivered.
• Set and champion technical standards and best practices — leading design and code reviews and raising the engineering bar across the Pod and Chapter.
• Embed AI and automation into pipeline development, testing, data validation, and monitoring to accelerate delivery and strengthen reliability.
• Provision reliable, well-structured data that brings data to insights, powering downstream analytics and AIenabled solutions.
• Evaluate and recommend new tools and improvements aligned with our data strategy.
• Uphold enterprise data governance and security — applying access controls, data classification, lineage, and quality controls to protect sensitive data and meet compliance requirements.
• Mentor colleagues on best practices and development techniques, ensuring knowledge stays with the team through our Chapters.

Requirements

Skills Required:

Core Data Platforms
• SAP HANA: extensive hands-on development experience with data modeling, ingestion, and transformation; strong grasp of performance tuning, security, and operability.
• SAP Data Services: proven experience building and maintaining ETL/data integration jobs, transformations, and data quality routines.
• Python: strong hands-on experience using Python to build and maintain ETL/data pipelines, transformations, automation, and validation.
• Stonebranch (or comparable workload automation / job scheduling tools): experience orchestrating and scheduling enterprise data pipelines.

Development & Technical
• Ability to independently design, build, test, and deploy end-to-end data pipelines and integrations.
• Deep knowledge of data modeling, transformation patterns, and enterprise integration technologies.
• Strong SQL skills for data transformation, querying, and performance optimization.
• Experience integrating data across APIs, databases, and enterprise applications.
• DevOps practices: Git-based source control (GitHub), CI/CD pipelines, automated testing, and environment promotion strategies.
• Command of data engineering best practices: reusability, error handling, logging, lineage, and secure credential management.

Troubleshooting & Support (critical)
• Lead investigation and resolution of complex production data incidents and quality issues.
• Strong root-cause analysis and remediation experience supporting business users.
• Ability to monitor pipeline performance and build observability (monitoring, alerting, quality checks) to proactively minimize downtime.

AI & Automation
• Comfort with AI tools (e.g., GitHub Copilot, ServiceNow AI) and eagerness to embed AI into development and operational workflows.
• Familiarity with intelligent automation capabilities such as automated data validation, anomaly detection, and AI-assisted development.
• Data-driven decision-making mindset.

Governance & Security
• Solid understanding of data governance, data quality, and data security best practices in an enterprise environment.
• Working knowledge of access controls, data classification, and lineage as applied to protecting sensitive data.

Tools & Collaboration
• Experience with work-tracking and documentation tools (Jira, Confluence, or equivalents) to manage tasks, track defects, and document solution designs and support runbooks.
• Version control and change/release management familiarity.

Delivery & Mindset Required:

• Outcome ownership: autonomy and accountability, driving work forward in a high-trust team.
• Technical leadership: sets standards and lifts the team through design/code reviews and mentoring, without needing formal authority.
• Cross-functional collaboration: thrives in a collaborative Pod with full accountability for deliverables.
• Delivery focus: strong ownership of timelines and quality.
• Continuous learning: stays current with evolving data and AI capabilities and applies them effectively.
• Adaptability: embraces change and contributes to continuous improvement.
• Communication: translates business needs into technical solutions, and presents technical detail to a non-technical audience.

Qualifications Required:

• Bachelor’s degree in Computer Science, Information Technology, or related field (or equivalent practical experience).
• Typically 6–8+ years of data engineering experience, including deep hands-on development with SAP HANA, SAP Data Services, and Python-based ETL.
• Demonstrated experience owning and supporting production data pipelines end to end, including operating under service levels.
• Track record of technical leadership through influence — mentoring, design reviews, and setting standards.
• Familiarity with Agile/Scrum delivery methodologies.

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