Valtech

Ecommerce

• Define and own the architectural patterns and best practices for enterprise Shopify Plus deployments, including headless/composable commerce, Shopify Functions, Markets Pro, and B2B capabilities.
• Lead the design of end-to-end commerce ecosystems spanning Shopify Plus and adjacent enterprise systems—ERP (SAP, Oracle, NetSuite), enterprise search (Elasticsearch, Algolia), CDP/CRM (Salesforce, Segment, mParticle), PIM, OMS, and payment gateways.
• Evaluate and stay ahead of emerging Shopify platform capabilities (e.g., Shopify Audiences, Checkout Extensibility, Editions releases) and translate them into client-ready solutions and internal playbooks.
• Serve as the practice’s ultimate authority on technical feasibility, scalability, and integration strategy for Shopify Plus engagements.
• Oversee technical delivery across global, cross-functional teams delivering enterprise Shopify Plus solutions, ensuring quality, performance, and client satisfaction at scale.
• Establish governance frameworks, risk mitigation strategies, and delivery KPIs tailored to the pace and complexity of commerce platform engagements. -
• Act as a technical escalation point and actively unblock delivery teams, modeling the kind of senior leadership and decisiveness the practice requires.&nbsp;
• Build, retain, and develop a high-performing team of Shopify architects, engineers, and solution leads—setting clear expectations, creating growth paths, and fostering a culture of technical excellence.
• Own the technical narrative in the sales process: participate actively in discovery, solutioning, estimation, architecture design, and proposal development for Shopify Plus pursuits.
• Partner closely with business development and account teams to identify upsell and cross-sell opportunities within existing Shopify Plus engagements.
• Serve as a credible, senior technical voice in client-facing conversations—building trust, managing expectations, and positioning our practice as a strategic partner, not just a vendor.
• Maintain and deepen relationships with Shopify’s partner and alliance teams, ensuring we are positioned for early access to new programs, beta capabilities, and co-sell opportunities.
• Mentor and coach Shopify architects, technical leads, and engineers across regions—actively investing in their growth and career trajectories.
• Drive Shopify-specific certification and enablement programs (e.g., Shopify Partner certifications, Shopify Developer certifications) and build learning paths that keep the team at the forefront of platform innovation.
• Contribute to internal knowledge sharing, solution playbooks, case studies, and reusable architecture assets that accelerate delivery and sales across the practice.

Practice Director

Program Manager

• Lead large, cross-functional programs from strategy through execution and delivery
• Define program vision, success metrics, and KPIs aligned with business and technical goals
• Partner with engineering, product, and data leadership to shape roadmaps and priorities
• Act as a strategic advisor, influencing long-term product and platform direction
• Drive data-informed decision-making, including experimentation strategies (A/B testing, measurement frameworks)
• Ensure programs leverage robust analytics, tracking, and reporting frameworks
• Identify opportunities for optimization through quantitative analysis and performance insights
• Guide teams in building scalable, data-driven platforms and systems
• Oversee multiple workstreams, managing timelines, risks, dependencies, and delivery milestones
• Establish strong governance, including executive reporting, communication cadence, and stakeholder alignment
• Ensure consistent delivery against quality, scope, and timeline expectations
• Proactively identify and resolve blockers across teams
• Provide oversight on architecture, scalability, and system design, particularly in cloud environments
• Review and challenge technical decisions to ensure alignment with best practices
• Collaborate with architects and engineering leads to ensure robust, secure, and scalable solutions

Technical Program Director

Solutions Engineer

• Provide hands-on architectural leadership embedded within client teams, from discovery through delivery
• Design and implement enterprise-scale cloud architectures, including application infrastructure, integration strategies, and secure deployment patterns
• Guide engineering teams on technical trade-offs, architecture decisions, and best practices
• Develop cloud adoption roadmaps, including hybrid and migration strategies where applicable
• Architect and support highly distributed, scalable systems in cloud environments
• Act as a trusted advisor to senior stakeholders, translating complex technical topics into actionable recommendations
• Partner with cross-functional teams to proactively manage risks, dependencies, timelines, and technical blockers
• Support the design and deployment of AI-enabled and data-driven solutions on GCP

GCP Solutions Architect

• As a Program Manager, lead the end-to-end delivery of data platform and cloud transformation projects or programs, from initiation through delivery to closure.
• Manage program level projects with competing priorities while maintaining momentum, quality, and stakeholder alignment.
• Translate client business objectives into structured, milestone-based delivery plans with clear acceptance criteria.
• Lead and align multidisciplinary teams across data engineering, cloud platforms, governance, QA, DevOps, and business stakeholders.
• Coordinate delivery across multiple release waves, ensuring consistency, predictability, and quality at scale.
• Enable teams to deliver their best work by providing clarity, structure, and strong delivery leadership.
• Own overall project or program health, including feasibility of delivery given scope, timeline, budget, resources, risks, and interdependencies.
• Proactively identify and mitigate program-level risks across technology, operations, and organizational change.
• Ensure that each milestone and release meets defined quality, governance, validation, and operational readiness standards.
• Build trusted relationships with senior client stakeholders, ensuring transparency, clarity, and confidence throughout the engagement.

Cloud Engineer

• Embed within customer engineering teams and lead technical discovery sessions with business stakeholders, engineering leadership, and security to translate ambiguous business problems into clear AI architectures and delivery plans.
• Architect, code, and ship production-grade agentic AI solutions on Google Cloud — including multi-agent systems, MCP servers, sub-agents, skills, connectors, agentic wrappers, and safety guardrails — that move customers beyond pilots into measurable business value.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines and grounding architectures, including chunking strategy, vector databases, and embedding optimization to prevent hallucinations and ensure response quality.
• Build the “connective tissue” between Google’s AI products and customer infrastructure, including APIs, legacy data silos, identity, and security perimeters.
• Implement multi-agent patterns such as ReAct, self-reflection, and hierarchical delegation using frameworks like Google’s Agent Development Kit (ADK) or LangGraph
• Build high-performance evaluation pipelines and observability frameworks for agentic systems, with attention to accuracy, safety, latency, cost-per-request, and tokens-per-second.
• Debug agent logic and optimize tool selection in live, high-traffic environments, including tracing conversation and request IDs across microservices to resolve production failures.
• Co-build with customer engineering teams and act as a hands-on advocate for AI-assisted development, introducing and operationalizing AI coding tools to accelerate delivery and elevate engineering practices.
• Drive a deliberate handoff to the customer’s team, ensuring long-term ownership, documentation, and end-user adoption after the engagement concludes.
• Develop and maintain technical documentation, architecture decision records, and evaluation results across all assigned engagements.

Forward Deployed Engineer, AI Expert

Procurement

• Lead the end-to-end sourcing cycle for Professional Services and Consulting
• Build and manage a preferred supplier panel
• Negotiate contract pricing, terms, and conditions directly with suppliers
• Enable efficient and effective purchase services
• Manage the portfolio of active agreements including scope, pricing, amendments, and renewals
• Drive portfolio consolidation projects
• Oversee the sourcing cycle and handle tenders
• Benchmark supplier rates and market positioning
• Maintain strong relationships with key internal stakeholders
• Evaluate and continuously improve procurement processes

Senior Procurement Specialist

Events

• Planning and executing various events across North America, such as roundtables, webinars, industry events and partner events.
• Developing event plans, budgets, and timelines – aligned with the NA marketing plan and organizational goals.
• Working with core teams of stakeholders, both from marketing and other Valtech teams (e.g. subject matter experts and the sales organisation).
• Defining event KPIs with marketing leads and key functions of the team.
• Collaborating with the global content team on event concept, themes, and design, all whilst maintaining branding consistency.
• Vendor management.
• Event production and logistics.
• On-site event management including setup, venue, tear down and guest management.
• Hosting post-event evaluation to gather feedback, identify areas for improvement and prepare event report.

Marketing Event Manager

Technical Project Manager

• support the Client domain portfolio
• drive cross-team initiatives
• manage both portfolio-level governance
• manage hands-on project delivery
• track technical activities across teams
• identify dependencies
• follow up on execution

Salesforce Developer

• Development of custom solutions using JavaScript, AMPScript, GTL, HTML, CSS, and SQL;
• Development of dynamic, personalized marketing assets such as emails, landing pages, web forms, and custom preference pages;
• Development and configuration tasks related to Marketing Cloud Engagement;
• Implementing integrations via FTP, APIs (SOAP and REST), and MC Connect;
• Staying up to date on Salesforce Marketing Cloud releases, new products, feature updates, and deprecations;
• Perform internal activities such as Individual Development Plans (PDI) and OKRs, paid training, reimbursement requests, software licenses and vacation requests, absence records, project time entries (timesheets), time entry adjustments, and other tasks according to the company's internal processes.

Junior Salesforce Marketing Cloud Developer

• Pilot e-commerce projects and platform rollouts across different international markets, managing scope, timelines, dependencies, and risks end to end.
• Coordinate market-specific delivery while maintaining alignment with the global platform and program standards.
• Ensure clear communication and reporting across business stakeholders, technical teams, and delivery partners.
• Track and manage features within the SAFe delivery framework, including participation in and preparation for PI Planning.
• Maintain visibility on feature progress, dependencies, and commitments across teams and program increments.
• Work closely with Product Owners, Scrum Masters, and delivery teams to keep delivery cadence and execution on track.
• Drive ongoing delivery across web performance, platform security, and test automation.
• Manage and follow up with external partners and system integrators, holding them accountable to quality and timelines.
• Champion continuous improvement of the e-commerce platform — stability, performance, quality, and operational efficiency.
• Partner closely with business, technical, and vendor teams to align priorities and unblock delivery.
• Act as a connection point between business needs and technical execution, clarifying ownership and driving follow-up.
• Bring an appetite for automation, AI, and new approaches to improve delivery efficiency and reduce manual effort.
• Identify opportunities to streamline delivery workflows, testing, and reporting through modern tooling and AI-assisted practices.

Technical Delivery Manager

Data Scientist

• The Senior AI Data Scientist is a senior individual contributor role within Data Science, AI, & Agentic. This role is responsible for leading complex analytical, predictive, and applied AI workstreams, operating with a high degree of independence and serving as a trusted advisor on data science and AI strategy.
• This role focuses on shaping and executing advanced modeling, experimentation, and applied AI solutions that address high-value business problems. The Senior AI Data Scientist translates ambiguous stakeholder needs into structured analytical approaches, model designs, evaluation frameworks, and actionable insights. They partner closely with cross-functional teams and stakeholders to ensure that analytical and AI-driven solutions are both technically sound and practically impactful.
• At this level, the role combines strong quantitative expertise with deep modeling capability, rigorous experimentation practices, and thoughtful application of AI methods. The Senior AI Data Scientist influences analytical direction, improves delivery quality through best practices and reusable approaches, and elevates the overall effectiveness of data science workstreams across engagements.

Senior AI Data Scientist

Data Engineer

• Lead the most complex and high-impact data architecture initiatives across clients, business areas, domains, platforms, or strategic programs.
• Define data architecture strategies that connect business goals, domain structures, semantic logic, platform realities, governance expectations, and downstream consumption needs.
• Serve as a senior advisor to internal and client stakeholders on architecture maturity, semantic structure, governance direction, platform-aligned design, and long-term maintainability.
• Establish and refine best practices for conceptual, logical, and physical data modeling, business entity design, semantic consistency, metric and dimension alignment, metadata expectations, domain boundaries, and reusable architecture patterns.
• Guide the design of scalable semantic structures, business concept frameworks, taxonomy and ontology-informed models, governed access patterns, and reference architectures that improve trust and downstream usability.
• Translate ambiguous executive and stakeholder questions into clear architecture approaches, semantic frameworks, platform strategies, governance-aligned design patterns, and business-relevant recommendations.
• Lead architecture reviews and design authority discussions to identify risks, resolve ambiguity, strengthen standards alignment, and improve long-term structural quality.
• Assess and shape how architecture choices support reporting, analytics, data products, machine learning, AI workflows, retrieval patterns, and agentic systems across structured, semi-structured, and selected unstructured data use cases.
• Provide governance direction through standards, design reviews, architecture guardrails, and decision frameworks while keeping hands-on governance execution lighter than framework and review ownership.
• Synthesize architecture tradeoffs, semantic implications, platform constraints, and governance considerations into clear insights, strategic implications, and recommended actions.
• Influence cross-functional teams across DEPA, DSAI, and AIO to improve how data platforms, governed data products, semantic layers, analytics, and AI workflows work together.
• Review major architecture deliverables to ensure quality, clarity, consistency, rigor, and practical business value.
• Contribute to thought leadership, growth initiatives, proposal strategy, solution shaping, and new business efforts where senior architecture expertise is required.
• Help create, improve, and promote reusable frameworks, templates, standards, semantic models, reference architectures, design review patterns, and accelerators that strengthen delivery consistency across the practice.
• Mentor senior practitioners and help define what excellent data architecture practice looks like across the organization.
• Reinforce strong governance, privacy, security, and data-quality expectations across engagements and teams.

Lead Data Engineer

AI Engineer

• Drive complex AI engineering workstreams across multiple business areas, use cases, products, or stakeholder groups.
• Define AI engineering approaches that align business goals, workflow opportunities, orchestration patterns, integration requirements, and measurable implementation outcomes.
• Translate ambiguous stakeholder needs into structured implementation plans, workflow logic, prompt orchestration, retrieval patterns, tool-calling approaches, and solution recommendations.
• Guide the implementation of AI-enabled applications, agents, assistant-style experiences, and workflow components that connect models to practical business processes.
• Establish and reinforce best practices for prompt orchestration, tool use, context handling, retrieval flow, API integration, workflow composition, response processing, testing, and code quality.
• Design and improve reusable implementation patterns for use cases such as summarization, extraction, question answering, search augmentation, conversational support, workflow automation, grounded insight delivery, and agent-assisted task execution.
• Contribute to delivery planning, prioritization discussions, and quality standards across AI engineering engagements.
• Mentor, coach, and support practitioners through feedback, guidance, and performance development.
• Review deliverables for clarity, technical rigor, quality, consistency, safety, and business usefulness.
• Help teams improve workflow reliability, integration quality, testing discipline, and implementation reuse across projects.
• Collaborate with AI Scientists, Data Scientists, Data Engineers, Analytics Engineers, and Architects to align AI solutions with business needs, governed data access, platform realities, and technical constraints.
• Contribute to hiring, onboarding, capability development, and team maturity within the AI engineering practice.
• Follow established governance, privacy, safety, and responsible AI-use standards across the work of the team.

Senior AI Engineer

• Lead complex data engineering workstreams across multiple business areas, source systems, domains, or stakeholder groups.
• Define data engineering approaches that align business needs, source-system realities, platform constraints, transformation patterns, and downstream consumption requirements.
• Translate ambiguous stakeholder needs into structured pipeline strategies, ingestion patterns, transformation designs, curated data layers, and actionable recommendations.
• Guide the design and implementation of scalable ingestion, transformation, and serving patterns across cloud warehouses, lakehouses, and analytical environments.
• Establish and reinforce best practices for schema design, pipeline modularity, layered data architecture, data validation, lineage awareness, scheduling discipline, error handling, and maintainable engineering patterns.
• Design and improve reusable data engineering workflows that support reporting, dashboarding, data science, AI workflows, and agentic use cases.
• Support team staffing, work allocation, prioritization, and delivery quality across data engineering engagements.
• Manage, coach, and develop practitioners through feedback, guidance, and performance support.
• Review deliverables for clarity, technical rigor, quality, consistency, and business usefulness.
• Help teams improve data quality, reduce pipeline fragility, strengthen source alignment, and increase trust in downstream governed datasets.
• Support data engineering patterns that improve AI and agentic readiness, including metadata-rich datasets, retrieval-supportive organization, document and chunk preparation support, governed access paths, and scalable data availability for downstream workflows.
• Collaborate with Analytics Engineers, Data Analysts, Data Scientists, AI Scientists, AI Engineers, AI Platform Engineers, and Architects to align data foundations with downstream analytical, AI, and platform needs.
• Contribute to hiring, onboarding, capability development, and team maturity within the data engineering practice.
• Follow established governance, privacy, security, and data-quality standards across the work of the team.

Senior Data Engineer

• Lead the design and delivery of enterprise AI applications, copilots, assistants, and agentic solutions.
• Define implementation strategies that align AI capabilities with business objectives and user needs.
• Guide teams in building scalable AI workflows, retrieval systems, orchestration layers, and tool-integrated experiences.
• Drive best practices across prompt engineering, retrieval-augmented generation (RAG), tool calling, context management, testing, observability, and evaluation.
• Partner with clients to understand business challenges and identify AI-powered opportunities.
• Translate ambiguous requirements into practical implementation approaches and technical roadmaps.
• Act as a trusted advisor during workshops, discovery sessions, and executive presentations.
• Collaborate closely with Data Scientists, Data Engineers, Architects, Analytics Engineers, Product teams, and business stakeholders.