Overview

You’ve Never Been Satisfied with “Valuable 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

About the Role

We are seeking a Data Engineer to support large-scale capital infrastructure programs, with a strong focus on data transformation and preparation to enable trusted, analytics-ready datasets for Power BI reporting. This role sits at the intersection of data ingestion, transformation, and analytics enablement, supporting engagements where owners consolidate data from internal enterprise systems and external supply-chain sources. Much of the value in this role comes from cleaning, standardizing, and conforming inconsistent data into common structures that support portfolio-level and executive reporting. You will work closely with Data & Analytics Consultants and BI Developers to design robust ingestion and transformation frameworks using Azure Data Factory (ADF), curate analytics-ready data in Snowflake, and ensure downstream Power BI reporting is accurate, performant,



and scalable. Responsibilities

Data Transformation & Preparation: Design and implement data transformation logic to cleanse, standardize, and conform data from both internal enterprise systems and external supply-chain sources. Normalize inconsistent supply-chain data into common dimensions, reference data, and standardized KPI definitions. Implement business rules, derivations, and calculations to ensure consistency across reports and dashboards. Manage slowly changing dimensions (SCDs), snapshots, and historical views to support trend and time-series analysis. Ensure all transformed datasets are analytics-ready and aligned to defined reporting use cases. Analytics Enablement & Reporting Support: Deliver Power BI–ready datasets aligned to standardized KPIs and reporting requirements. Design and maintain analytics-focused data models (facts, dimensions, snapshots) optimized for reporting performance. Support Power BI refresh strategies, including incremental refresh and dependency sequencing. Partner closely with BI developers and analysts to ensure semantic consistency between Snowflake data models and Power BI measures. Work within the client’s Snowflake and Power BI environment, writing complex SQL transformations to prepare EPC and PMIS data (e.g., Oracle Primavera P6 and related systems) for reporting. Data Engineering & Architecture:



Develop and maintain end-to-end ELT pipelines using Azure Data Factory (ADF) into Snowflake, in support of transformation and reporting needs. Implement incremental loads, change data capture (CDC), and historical tracking to enable time-based analysis. Support scalable and maintainable data architectures aligned to enterprise analytics standards. Internal & Supply Chain Data Ingestion Automation (Enabling Capability): Design and implement automated ingestion pipelines for: Internal enterprise systems (e.g., PMIS, ERP, scheduling, commercial systems), and External supply-chain data (e.g., contractor cost, progress, schedule, commercial, and performance data). Handle ingestion from heterogeneous sources, including databases, flat files, APIs, and shared data environments. Build reusable Azure Data Factory (ADF) pipeline templates to support multi-system, multi-vendor, and multi-project ingestion patterns. Implement validation, reconciliation, and exception handling to manage inconsistent, late, or partial data submissions. Data Quality, Governance & Reliability: Embed automated data quality checks within ingestion and transformation pipelines. Monitor pipeline execution and proactively address failures, anomalies, and data integrity issues. Support data lineage, documentation, and governance standards across both internal and externally sourced data. Enforce consistent data structures, naming conventions, and KPI definitions. Process Oversight & Delivery: Collaborate with technical teams to monitor CI/CD pipelines (GitHub / Azure DevOps) and troubleshoot issues impacting reporting availability. Work closely with Data & Analytics Consultants to translate reporting requirements into scalable transformation and modeling solutions. Participate in Hybrid-Agile delivery, incl

📌 Data Engineering Specialist (Toronto)
🏢 Accenture
📍 Toronto