{"id":3300993,"url":"https://alion.io/job/shyftlabs-forward-deployed-data-architect","title":"Forward Deployed - Data Architect","company":{"id":685982,"name":"Shyftlabs","domain":"shyftlabs.io","url":"https://alion.io/company/shyftlabs","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":91,"open_postings":25,"ghost_share":0,"stale_share":0.36,"repost_share":0,"time_to_fill_p50_days":39,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":["CA"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":76000,"max_usd":154000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":10},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"Git","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-26T21:48:22Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-30T06:44:13Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":2},"description":"The Opportunity\nShyftLabs is a fast-growing data and technology company partnering with Fortune 500 organizations to deliver innovative, data-driven solutions. We specialize in building scalable, enterprise-grade platforms that help businesses unlock the value of their data through modern cloud technologies, advanced analytics, and AI-driven capabilities.\nWe are seeking an experienced Data Architect to work directly with enterprise clients, providing architectural leadership and hands-on technical expertise across modern data engineering initiatives. This is a client-facing role that combines solution architecture, technical consulting, and delivery oversight, requiring the ability to translate complex business requirements into scalable, secure, and reliable data solutions.\nYou will collaborate closely with client stakeholders, engineering teams, and technology partners to define architectural direction, establish engineering standards, and guide data platform implementations from design through production. You will also review partner-developed architectures, code, data models, and pipelines to ensure solutions meet enterprise requirements and align with established best practices.\nThe ideal candidate is a technically strong, hands-on architect who can confidently engage with senior client stakeholders, communicate complex technical concepts clearly, and influence architectural decisions while supporting distributed engineering teams.\nWhat You'll Be Doing\nLead Client-Facing Architecture Discussions: Work directly with client stakeholders to understand business objectives, technical requirements, existing data environments, and architectural challenges. Translate these needs into practical, scalable data architecture solutions.\n\nDefine Enterprise Data Architecture: Design and recommend modern data platform architectures using Azure Databricks, Delta Lake, and cloud-based data engineering technologies, ensuring alignment with client objectives and enterprise standards.\n\nDrive Technical Discovery and Solution Design: Facilitate technical workshops, discovery sessions, and architecture reviews to assess current-state environments, identify gaps, evaluate solution options, and define target-state architectures.\n\nProvide Hands-On Technical Leadership: Develop, validate, and review data engineering solutions using SQL, Python/PySpark, Apache Airflow, and dbt Core, balancing architectural oversight with practical implementation expertise.\n\nGuide Data Pipeline Development: Establish architecture and implementation standards for ingestion pipelines that integrate APIs, databases, files, and cloud data sources using Databricks-native capabilities such as Lakeflow Connect and Auto Loader.\n\nEstablish Data Architecture Standards: Define and promote reusable architectural patterns, implementation guidelines, and engineering best practices across client projects and distributed delivery teams.\n\nImplement Medallion Architecture: Design and guide the implementation of Bronze, Silver, and Gold data layers to support reliable ingestion, standardized transformations, curated data products, and downstream analytics.\n\nDrive Data Governance and Modeling: Establish appropriate practices for Unity Catalog, data access controls, lineage, semantic modeling, data discoverability, and enterprise data governance.\n\nOversee Technical Delivery and Quality: Review partner and engineering team deliverables, including architecture designs, code, data models, and pipelines, ensuring alignment with requirements, performance expectations, security standards, and long-term maintainability.\n\nChampion Data Quality and Reliability: Define standards for data validation, automated testing, pipeline monitoring, error handling, and operational readiness to ensure reliable production data products.\n\nSupport CI/CD and Engineering Automation: Guide implementation of Git-based workflows, automated testing, CI/CD pipelines, and controlled deployment practices across development, testing, and production environments.\n\nAdvise on Performance and Cost Optimization: Evaluate platform performance, processing efficiency, scalability, and cloud consumption, recommending improvements that balance technical requirements with business value.\n\nDrive AI-Assisted and Spec-Driven Development: Encourage the effective use of AI-assisted development tools and specification-driven engineering practices while maintaining architectural integrity, code quality, and delivery standards.\n\nCoordinate Distributed Engineering Teams: Provide technical direction, resolve architectural questions, and facilitate collaboration between client stakeholders, internal engineers, and external technology partners.\n\nCommunicate Progress and Technical Risks: Clearly communicate architectural decisions, implementation trade-offs, dependencies, risks, and recommendations to both technical and non-technical stakeholders.\n\nSupport End-to-End Delivery: Maintain architectural ownership throughout the project lifecycle, from discovery and solution design through implementation, deployment, and ongoing optimization.\n\nWhat You'll Bring\n8+ years of overall experience in data engineering, data architecture, or related technical roles, with strong hands-on experience designing and delivering production-grade data platforms.\n\nProven experience in a client-facing technical role, such as Data Architect, Solutions Architect, Technical Consultant, or Lead Data Engineer, working directly with enterprise customers or stakeholders.\n\nStrong hands-on expertise with Azure Databricks, including the design and implementation of scalable enterprise data solutions.\n\nDeep understanding of Delta Lake, including data management, processing patterns, and performance optimization.\n\nExperience with Unity Catalog, including data governance, access controls, lineage, and enterprise data management.\n\nStrong proficiency in SQL and Python/PySpark for data processing, transformation, and pipeline development.\n\nHands-on experience with Apache Airflow for orchestration and dbt Core for data transformation, testing, and modeling.\n\nExperience designing ingestion pipelines across APIs, relational databases, files, and other enterprise data sources.\n\nFamiliarity with Databricks-native ingestion technologies, including Lakeflow Connect and Auto Loader.\n\nStrong understanding of Medallion Architecture, data products, semantic modeling, and data quality frameworks.\n\nExperience with CI/CD, Git, automated testing, monitoring, and production data pipeline management.\n\nDemonstrated ability to review and challenge technical architectures, code, data models, and engineering deliverables while providing constructive, actionable feedback.\n\nExperience translating business requirements into technical specifications, architectural designs, and implementation roadmaps.\n\nAbility to evaluate technical trade-offs and communicate recommendations clearly to technical teams, business stakeholders, and client leadership.\n\nExperience working with external technology partners, third-party delivery teams, or distributed engineering organizations.\n\nFamiliarity with AI-assisted development tools and spec-driven development practices.\n\nStrong communication, presentation, stakeholder management, and problem-solving skills.\n\nAbility to manage competing priorities, navigate ambiguity, and build trusted relationships with clients while maintaining technical credibility.\n\nSalary Range\n$160,000 - $190,000 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