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Capgemini

Global consulting, technology services, outsourcing

Data Solution Architect

Data EngineeringUpdated on Nov 14
Full-timeNo salary listed

About the Job

Requirements

  • A minimum of 7 years of experience as a Data Architect designing, developing, and implementing cloud data solutions is required.
  • Demonstrated expertise in data platforms and a strong understanding of data warehousing concepts and practices are required.
  • The role requires the ability to design, monitor, and optimize data pipelines and infrastructure by analyzing performance metrics, identifying bottlenecks, and implementing efficiency and scalability improvements.
  • Experience designing and overseeing secure data warehouses and data lakehouses, including implementing data models, data quality checks, and governance practices, is required.
  • Experience collaborating with cross-functional teams, including business analysts, data scientists, and DevOps engineers, to implement data platforms is required.
  • Relevant architecture certifications from leading cloud platform providers are required.
  • Specialist experience in two or more of Azure, Databricks, or AWS is required.
  • For Azure: experience working with Microsoft Azure PaaS and IaaS, specifically App Services, Azure SQL Database, SQL Server, Storage, Synapse Analytics, Purview, Databricks, Azure Data Factory, Azure Data Lake, and Fabric, and understanding which Azure data components to use for different purposes.
  • For Azure: Microsoft certified architect.
  • For Databricks: experience designing and building high-performance data platforms using Databricks and Apache Spark to extract, transform, and load data into Azure Data Lake Storage and other Azure services.
  • For Databricks: ability to design, build, and deploy AI/ML models by integrating machine learning into data pipelines and using Databricks ML and Azure ML to develop predictive models and drive business insights.
  • For AWS: hands-on experience with AWS services such as Redshift, Glue, Lake Formation, SageMaker, and Athena.
  • For AWS: AWS Certified Architect.
  • Eligibility to obtain UK Security Check (SC) clearance is required, including continuous UK residence for the previous five years and satisfaction of the other clearance criteria.
  • Pre-employment checks include identity, nationality or immigration status, employment history covering three continuous years, and an unspent criminal record check.

Responsibilities

  • Lead data platform architecture by owning the design and implementation of cloud-native data platforms and driving modernization strategies that move legacy systems to scalable, cloud-native solutions.
  • Design lakehouse architectures that unify data lakes and warehouses to support advanced analytics, machine learning, and generative AI workloads.
  • Define AI-native and agentic reference architectures, including retrieval and retrieval-augmented generation platforms, embeddings and vector search, feature and model serving, and patterns for exposing governed data to AI agents, for example through the Model Context Protocol, with appropriate guardrails and human oversight.
  • Own data governance and trust by establishing lineage, access control, data quality, and auditability using tools such as Unity Catalog and Microsoft Purview, aligned to responsible AI expectations and emerging regulation such as the EU AI Act and the UK's approach.
  • Provide technical pre-sales leadership by shaping solution direction, proposals, costing, and estimates, and acting as the technical liaison between clients, sales, and delivery teams through contracting.
  • Set standards and optimize by defining reusable patterns, reviewing designs against the architectural blueprint, and monitoring and tuning performance, cost, and scalability across platforms.
  • Mentor and collaborate with data engineers, data scientists, platform engineers, and business analysts, growing capability and keeping solutions aligned to the overall architecture.
  • Track developments in data and AI and bring pragmatic recommendations.
  • Design and oversee delivery of secure data warehouses and data lakehouses, implementing data models, data quality checks, and governance practices to ensure reliable and accurate data.
  • Collaborate with cross-functional teams, including business analysts, data scientists, and DevOps engineers, to deliver data platform implementations.
  • Use Databricks and Apache Spark to build data platforms and load data into Azure Data Lake Storage and other Azure services.
  • Integrate machine learning into data pipelines and use Databricks ML and Azure ML to develop predictive models and drive business insights.
  • Work hands-on with AWS services, including Redshift, Glue, Lake Formation, SageMaker, and Athena.

Desired Qualifications

  • A background in the public sector or other regulated industries is especially valued.

About the Company

Capgemini

Global consulting, technology services, outsourcing

Capgemini provides consulting, technology services, and outsourcing to help organizations transform digitally. Its offerings include strategy and transformation consulting, system integration, application development, IT infrastructure management, and managed IT operations. It differentiates itself with a global presence in over 50 countries and a focus on applying AI, cloud, and cybersecurity to real business problems. Its goal is to help clients navigate digital transformation and improve efficiency and innovation through tailored, contract-based or managed services.

Company size
10,001+
Company stage
IPO
Headquarters
Paris, France
Founded
1967

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