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
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.
- (Internal Title: Business System Configuration / Development II)
- 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, including sprint planning, backlog refinement, and iterative releases.
- Contribute to CI/CD practices for ADF pipelines, Snowflake objects, and analytics assets.
- Working Conditions:
- Hybrid: this role's work location is primarily based in downtown Toronto but will see some travel to various client sites at key project milestones.
- Experience:
- 5+ years of experience as a Data Engineer or similar role.
- Advanced-level SQL with proven experience in Snowflake-specific SQL syntax, functions, and query optimization
- Experience ingesting structured files using Snowpipe, COPY INTO, External Tables, or similar mechanisms
- Strong understanding of Snowflake architecture, including Virtual Warehouses, Storage/Compute separation, Micro-partitioning, and Time Travel
- Experience with data modeling best practices (star schema, SCDs, conformed dimensions) in Snowflake
- Hands-on experience building ETL/ELT workflows with dbt, Azure Data Factory, or custom SQL jobs targeting Snowflake
- Comfort working with stakeholders across technical and business teams to clarify logic and validate outputs
- Experience with JIRA, Git, and collaborative development practices
- Education:
- Bachelor’s degree in Computer Engineering, Data Science, or related discipline; Master’s degree preferred
- CSM, ITIL V4, and certifications in tools like Microsoft SQL Server, Power BI, Microsoft Fabric, Azure, or GCP
- Nice to Have:
- Exposure to Project Management Information Systems like Oracle Primavera P6, Deltek Cobra, EcoSys, etc.
- Snowflake certifications such as SnowPro Core, SnowPro Advanced: Data Engineer, or other Snowflake specialty certifications or performance optimization badges are a strong asset
- Experience supporting Power BI projects or building models that feed enterprise dashboards
- Familiarity with SharePoint-based data delivery or M365 ecosystems
- Understanding of capital project controls, scheduling, procurement, or commissioning
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.
The recruiting efforts for this position are intended to fill an existing position.
The base pay range shown below is intended as a guideline to reflect the majority of offers for this role. It does not represent a maximum limit — in some cases, actual compensation may exceed the range where appropriate.
Role Location Annual Salary Range
Toronto $82,000 to $123,000
