Staff Data Developer
Full-time
AtkinsRéalis
Job Description Overview AtkinsRéalis is a world-leading engineering services and nuclear company dedicated to engineering a better future for our planet and its people. The Data, AI & Advanced Analytics team delivers enterprise data platforms, analytics, automation, and AI capabilities that support critical business processes across finance, operations, projects, people, and digital services. We are seeking talented technology professionals who are passionate about solving complex challenges using modern cloud and data technologies. We are looking for a practical, collaborative, and technically strong Staff Data Developer who can lead complex engineering work while remaining close to delivery and operations. The role supports enterprise-scale data integration, data quality, master data, operational data stores, analytics, and platform initiatives across multiple business domains. Provide senior hands-on engineering leadership for the Orb enterprise data platform, combining solution delivery, technical assurance, production support, release coordination, and coaching across Azure-based data integration and analytics services. Your role
- Own and deliver end-to-end data engineering changes across source-to-raw (S2R), raw-to-integrated (R2I), integrated-to-curated (I2C), and related data flows.
- Design, build, review, test, and optimize Azure Data Factory pipelines, SQL transformations, Azure Functions, curated datasets, and supporting orchestration.
- Troubleshoot failed or long-running pipelines, source-data issues, dependency problems, duplicate records, key design issues, refresh failures, and environment-specific configuration defects.
- Act as a senior technical escalation point for Orb incidents and production issues, completing root-cause analysis, implementing fixes, validating outcomes, and coordinating follow-up with analysts, QA, platform engineers, and source-system teams.
- Prepare and review deployment plans; coordinate pull requests, code reviews, environment promotion, production releases, rollback readiness, and post-deployment sanity checks.
- Monitor platform operations through Azure and platform logs, identify usage or performance patterns, and recommend changes to schedules, dependencies, batch processing, retries, and load strategies.
- Apply data classification, sensitivity, access, and governance requirements consistently across raw, integrated, curated, and consumer-facing data assets.
- Support data modelling and data quality decisions, including primary keys, business keys, mapping rules, source-to-target metadata, matching logic, and duplicate handling.
- Partner closely with data analysts, data architects, QA analysts, product owners, business stakeholders, and technical teams to clarify requirements and translate them into dependable engineering solutions.
- Provide technical coaching, code-review feedback, troubleshooting support, and knowledge transfer to engineers and analysts across Canada, the United Kingdom, and India.
- Maintain high-quality technical documentation, including mapping specifications, deployment plans, change records, operational evidence, and support handover material.
- Contribute to the evolution of the Orb platform, including standardized engineering templates, automation, maintainability improvements, and migration toward Microsoft Fabric.
- Build and rework Azure Data Factory (ADF) pipelines and supporting SQL or Azure Function components for REST APIs, files, ERP, HR, finance, project, procurement, and other enterprise sources.
- Review pull requests, validate implementation against engineering patterns, confirm unit testing, and identify defects before release.
- Coordinate DEV, UAT, pre-production, and production deployment activities.
- Refresh or rerun pipelines.
- Perform technical checks.
- Support controlled recovery activities.
- Contribute to source-to-target mappings, metadata, keys, sensitivity classifications, integrated and curated models, and reconciliation logic.
- Investigate failed loads, timeouts, API limits, duplicates, missing data, configuration issues, and cross-environment differences.
- Unblock analysts and engineers, explain platform behaviour, provide practical guidance, and preserve continuity across global handoffs.
- Significant hands-on experience delivering and supporting enterprise data platforms in Microsoft Azure.
- Data Engineering and Integration:
- Azure Data Factory.
- Azure Synapse Analytics.
- Azure SQL.
- SQL Server.
- Azure Data Lake Storage Gen2.
- ETL/ELT, orchestration, incremental and full-load patterns, dependency management, retry and batching strategies.
- T-SQL, stored procedures, source-to-target mapping, dimensional and relational data modelling.
- REST API, file-based, database, ERP, and SaaS integration patterns.
- Delta Lake, Parquet, Operational Data Store, Data Warehouse, Lakehouse, and curated-data concepts.
- C#/.NET and Azure Functions.
- Git and Azure DevOps pull requests, code reviews, build and release pipelines.
- CI/CD, environment configuration, release planning, deployment evidence, rollback, and post-release validation.
- PowerShell or equivalent scripting for automation and operational support.
- Production troubleshooting, root-cause analysis, logging, monitoring, and incident resolution.
- Data quality investigation, reconciliation, duplicate handling, key design, and validation.
- Data classification, sensitivity, secure access, Azure Key Vault, and Microsoft Entra ID.
- Clear technical documentation and disciplined change-control practices.
- Secondary/Developing Skills:
- Microsoft Fabric, including Data Factory, OneLake, Lakehouse, Warehouse, notebooks, pipelines, and semantic models.
- Power BI semantic models and downstream analytical consumption.
- Master Data Management concepts and Profisee.
- Real-time and event-driven data processing where required by the platform roadmap.
- Ability to move between solution delivery and operational support without losing engineering discipline.
- Ability to communicate clearly with technical and non-technical stakeholders and turn ambiguous issues into actionable engineering work.
- Strong judgement when assessing release readiness, production risk, data correctness, and appropriate escalation.
- Comfort working across time zones and coordinating handoffs among globally distributed engineering, QA, analysis, and architecture teams.
- A collaborative approach to coaching others, sharing context, and improving repeatability through standards and automation.
- Bilingualism (french and English)
Vacancy posted 11 hours ago
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