Principal Data Architect

Why Valtech? We’re the experience innovation company - a trusted partner to the world’s most recognized brands. To our people we offer growth opportunities, a values-driven culture, international careers and the chance to shape the future of experience.

At Valtech, you’ll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking or building the next generation of customer experiences, your work will help transform industries.

As a Principal Data Architect at a digital transformation agency, you’ll define and lead data architecture strategy across multiple client engagements—shaping modern data platforms, guiding delivery teams, and acting as a trusted advisor to senior client stakeholders. You’ll balance hands-on architecture with leadership: setting standards, assuring quality, and helping teams ship secure, scalable, and cost-effective data solutions.

• Architecture leadership:
  Define target-state data architecture and roadmaps across cloud and hybrid environments; align to business outcomes and transformation goals.
• Platform design:
  Architect modern data platforms (lakehouse/warehouse, streaming, batch, semantic layers) with clear patterns for ingestion, modeling, governance, and consumption.
• Data modeling:
  Lead conceptual/logical/physical modeling, domain-oriented design, and analytical modeling (dimensional, Data Vault, wide-table patterns where appropriate).
• Integration & interoperability:
  Design APIs, event-driven/streaming architectures, data sharing patterns, and integration with enterprise apps and SaaS products.
• Governance & trust:
  Establish data governance, metadata management, lineage, MDM/reference data approaches, data quality frameworks, and stewardship operating models.
• Security & compliance:
  Embed security-by-design (IAM, encryption, secrets, network controls), privacy-by-design, and regulatory requirements (e.g., GDPR) into architectures.
• Delivery assurance:
  Provide technical oversight across projects—review designs, ensure best practices, manage architectural risks, and unblock teams.
• Client advisory:
  Lead architecture workshops, produce decision papers and architecture artifacts, present to C-level stakeholders, and influence investment decisions.
• Engineering collaboration:
  Partner with data engineers, analysts, ML engineers, and product teams to ensure designs are buildable, operable, and meet SLAs.
• Observability & operations:
  Define operational models (Data Ops/MLOps), monitoring, incident response, SLOs, and cost/performance optimization.
• Practice building:
  Contribute to agency accelerators, reusable reference architectures, pre-sales discovery, proposals, and mentoring/coaching.
• Enterprise and solution architecture diagrams, reference architectures, and platform blueprints
• Data strategy and roadmap documents, capability assessments, and options appraisals
• Standards and guardrails (naming, modeling, security, SDLC/Data Ops, CI/CD patterns)
• Data governance operating model, catalog/lineage approach, quality KPIs
• Non-functional requirements: performance, resilience, DR, availability, and cost model
• Architecture Decision Records (ADRs) and assurance reviews

To be considered for this role, you must meet the following essential qualifications:

• 10+ years in data architecture / data engineering, including leading architecture for complex programs
• Proven design of cloud data platforms (AWS/Azure/GCP) and modern analytics stacks
• Strong command of data modeling, distributed systems concepts, and integration patterns
• Experience with governance, metadata/lineage, data quality, and privacy/security controls
• Ability to communicate clearly with both engineers and senior business stakeholders
• Consulting experience: multi-client delivery, ambiguity management, workshop facilitation, and influencing without authority
• Strong written skills: concise architecture documentation and decision-making artifacts

Common technology exposure (varies by client)

• Data platforms:
  Databricks, Snowflake, Big Query, Synapse/Redshift, Fabric, lakehouse…