You've Never Been Satisfied with "Good Enough."

You want to make an impact, not just manage projects, but change how the world gets built. At Accenture Infrastructure & Capital Projects, you'll do exactly that. You'll help develop and deliver the factories, grids, transit systems, and public infrastructure that keep communities moving - and do it smarter, safer, and more sustainably than ever before.

You’ll work alongside people who think big and act bold - project managers, engineers, technologists, and strategists who blend real-world experience with digital innovation and AI. Together, we're transforming how capital projects are planned, managed, and executed, creating a better way to build for the future.

Because "good enough" builds the past. You're here to build what's next, on a team that outperforms every norm.

Visit us here to learn more about Accenture Infrastructure & Capital Projects

- (Internal Title: Business System Configuration / Development II)
- Data Science and Strategic Support
- Assist the Data Science Manager in achieving objectives and support the development and execution of strategic roadmaps for data management and mobilization.
- Own end-to-end delivery for defined data domains, such as cost, schedule, commitments, risk, and change, including ingestion, transformation, publishing, and ongoing operational support.
- Translate requirements from Performance Analytics & Insights into clear data contracts, scalable engineering solutions, and analytics-ready data products.
- Coordinate upstream changes with Business Systems and source system owners to maintain stable interfaces and minimize disruption to downstream consumers.
- Ensure curated data products are documented, fit for purpose,



and adopted by downstream reporting and analytics consumers.

- Collaboration and Teamwork

- Collaborate closely with multidisciplinary teams to foster a high-performing and collaborative workplace.
- Partner with Project Controls subject matter experts to ensure datasets reflect controlled baselines, approved business logic, and established governance without replacing control authority.
- Work with governance stakeholders to align data retention, access, classification, and usage with organizational policies.
- Support enablement by presenting data products to consumers, documenting recommended usage patterns, and contributing to continuous learning initiatives.
- Lead cross-functional troubleshooting and incident resolution for owned data domains, communicating impacts, recovery actions, and follow-up improvements.

- Data Architecture and Management

- Ensure scalable and flexible data architecture that supports operational excellence, data reliability, auditability, and cost-effective growth.
- Design incremental refresh strategies using appropriate patterns such as change data capture, snapshots, and partitioning to improve performance and traceability.
- Implement orchestration patterns covering dependencies, retries, backfills, scheduling, and recovery, with clear runbooks, alerts, and operational readiness procedures.




- Implement CI/CD practices for data pipelines, including automated testing, deployment automation, versioning, and controlled promotion across environments.
- Monitor platform and pipeline performance, tune compute and storage consumption, and introduce observability through metrics, logs, traces, dashboards, and alerts.
- Participate in creating and maintaining data dictionaries, catalogs, source mappings, lineage, transformation logic, assumptions, and known limitations.
- Support efforts to reduce data debt by optimizing data structures, standardizing engineering patterns, and strengthening management controls.

- Analytics and Insights

- Apply advanced data modeling expertise to design and optimize dimensional or domain data structures for effective storage, retrieval, analysis, and scalable reporting.
- Design models that align to business hierarchies and control structures, including WBS/CBS, activity codes, portfolio structures, and other approved enterprise dimensions.
- Implement conformed dimensions and standardized measures to support consistent cross-program analytics and reusable semantic foundations.
- Define and implement validation routines appropriate to dataset criticality, including tolerance checks, reconciliation, completeness checks, and anomaly-detection triggers.
- Apply performance optimization techniques such as partitioning, clustering, caching, and efficient transformation patterns.
- Support audit requests by demonstrating lineage, transformation evidence, and reconciliation for key reported figures.
- Proactively detect and reduce data quality issues through automated checks, stronger con

📌 Infrastructure & Capital Projects – Data Engineering Specialist, COM (Toronto)
🏢 Accenture
📍 Toronto