Lead Data Architect

Role overview

Client delivery and solution architecture

• Own technical data solution delivery and execute data strategies that support client outcomes and growth.
• Architect data products that build client trust and accelerate data-driven decisions.
• Lead strategic client discussions and translate ambiguous business problems into data engineering solutions.
• Run discovery workshops to identify core problems and opportunities for AI-powered data architectures.
• Design AI-native, cloud-forward data platforms and automated data ecosystems grounded in client processes.
• Deliver production-grade automation and real-world data flows rather than conceptual designs alone.
• Use cloud providers, modern data-stack partners, and AI platforms to select pipeline and storage capabilities.
• Guide technical choices by balancing innovation with scalability, cost-performance, and reliable execution.

Technical leadership and data foundations

• Mentor and guide data engineers and help develop engineering practices through technical contribution and example.
• Design modular data products across ingestion, transformation, and consumption, embedding data quality, lineage, and privacy by design.
• Build AI-ready foundations connecting structured data with unstructured and vector stores for RAG and generative AI workflows.
• Collaborate across product, engineering, data science, and delivery teams, sharing expertise in DataOps, infrastructure as code, and automated quality.
• Design resilient modern data architectures using cloud platforms and data-stack tools; the description cites Snowflake, Databricks, SQL, Python, Spark, and dbt.
• Translate architecture decisions into measurable business outcomes and use cost-performance benchmarks to assess transformations.

Functional and technical capabilities

• Consultative data architecture and solution design; client discovery and identification of business, data, and system opportunities.
• Architectural storytelling that explains Lakehouse, Data Mesh, RAG, partitioning, and schema design to senior technical and business stakeholders.
• Technical negotiation and facilitation of sound architectural decisions across client and internal teams.
• Cloud-native data engineering, distributed compute, schema evolution, and architectures designed to scale to petabyte volumes and data skew.
• Major cloud platforms include GCP, AWS, and Azure; modern data platforms include Snowflake and Databricks.
• Data engineering tools cited include SQL, Python, Spark, and dbt.
• Capabilities include Lakehouses, real-time streaming, platform migrations, generative AI, RAG pipelines, and vector databases.
• Data governance includes data quality, lineage, privacy by design, GDPR, and PII considerations.

Experience and qualifications

• 8+ years of progressive experience driving AI-driven, cloud-native data transformations and delivering data products.
• Proven record architecting complex platforms such as Lakehouses, real-time streaming systems, and platform migrations.
• Direct experience as a lead builder and consultative architect, acting as a technical authority for clients.
• Expert-level mastery of major cloud platforms, GCP, AWS, or Azure, and the modern data stack.
• Strong background in enterprise data platforms, applied AI/ML, generative AI integration, or large-scale cloud data migration.
• Evidence of leading end-to-end transformations and demonstrating architecture value with cost-performance benchmarks.
• Production-grade automation across telecom, fintech, healthcare, or retail is a bonus.
• Enterprise-scale transformation contributions and experience scaling software or engineering initiatives are presented as advantageous.

Working approach

About the employer

Application

• The posting invites applicants to apply and include a statement alongside their resume.