Accenture Construct – Data Engineering Specialist, COM Administration | Information Technology | Project Controls | Project Management | Technical Advisory | Full-time 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. 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. ~ Ensure curated data products are documented, fit for purpose, and adopted by downstream reporting and analytics consumers. 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 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. Apply advanced data modeling expertise to design and optimize dimensional or domain data structures for efficient 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 productive 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 controls, root-cause analysis, and corrective actions. Support the development of advanced reporting strategies using data engineering and analytics engineering practices to improve efficiency and consistency. Prepare semantic model foundations for BI tools through clear naming conventions, standard aggregations, reusable measures, snapshot strategies, and documented business definitions. Ensure transformations and models follow agreed standards for naming, lineage, versioning, testing, and maintainability. Support self-analytics enablement through training, documentation, recommended usage patterns, and close collaboration with reporting consumers. Deliver engineering changes through disciplined release practices that minimize disruption and provide clear rollback and recovery procedures. Office-based (5 Days a week in office) Experience: 3-7 years of experience in data engineering, analytics engineering, or software engineering with a strong data focus. Bachelor’s degree in Computer Engineering, Data Science, Software Engineering, or a related discipline

• Master’s degree preferred.

Azure Data Engineer

Associate or equivalent Azure/Fabric data engineering certification; SQL and Advanced Data Modeling training strongly preferred. CSM, ITIL V4, and other relevant Microsoft, AWS, Azure, or GCP certifications are assets.

Microsoft

Fabric, including strong hands‑on experience building and operating scalable data engineering solutions across Fabric workloads. SQL and advanced data modeling, including dimensional modeling, conformed dimensions, standardized measures, lineage, and semantic model readiness. ETL/ELT processes, data governance, data quality, data integrity, reconciliation, and automated validation controls.

Azure data engineering services, Synapse Pipelines, Data Marts, Power Platform, and related cloud data solutions. Power BI and Tableau, including data modeling, semantic foundations, visualization, and report enablement. Python, Power Query, DAX, and advanced Excel for data transformation, analytics, automation, and investigation.

Incremental processing patterns such as change data capture, snapshots, partitioning, and performance optimization.

Experience in transit, transportation, infrastructure, capital projects, or project controls domains is advantageous but not mandatory.



Analytical and Software Skills Strong problem‑solving and analytical skills with a results‑oriented mindset and demonstrated ability to troubleshoot complex pipeline, data quality, and performance issues. Strong SQL skills and practical experience designing scalable data pipelines, transformation logic, and analytical data models.

Substantial experience with project control tools and dashboarding software, with the ability to translate business and control requirements into governed data solutions. Ability to communicate technical concepts, operational impacts, and data limitations clearly to both technical and business stakeholders. Familiarity with orchestration and streaming patterns and technologies such as Airflow, Kafka, or Kinesis, including monitoring, retry, backfill, and incident‑response practices.

Familiarity with Microsoft SharePoint, Primavera P6, Earned Value Management software, and enterprise data governance tooling is a plus.

Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation, based on full‑time employment, for roles that may be hired as set forth below. If you require accommodation during the recruitment, assessment or selection process for a disability or other protected ground under applicable human rights legislation, please notify your recruiter or contact us toll free at 1 (877) 889-9009.

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Accenture

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📌 Accenture Construct - Data Engineering Specialist, COM (Toronto)
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📍 Toronto