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 â 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 â 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 â 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:
⢠Bachelorâs degree in Computer Science, Information Technology, or related field (or equivalent practical experience).
⢠Typically 6â8+ 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 â mentoring, design reviews, and setting standards.
⢠Familiarity with Agile/Scrum delivery methodologies.
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