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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing enterprise data architectures, including lakehouse and data warehouse solutions, while ensuring data governance, quality, and security. Proven ability to lead modernization efforts and engage with customers to deliver tailored data strategies and solutions.
Highest-signal resume keywords
Data ArchitectureDatabricksSnowflakeMicrosoft FabricData Governance
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
SQLPythonData ModelingETL/ELTData IntegrationData TransformationData SecurityCloud PlatformsData ObservabilityDataOps
Soft Skills
Customer EngagementCommunicationWorkshop FacilitationCollaborationMentoring
Tools & Technologies
AzureAWSGoogle CloudApache KafkaBI Tools
Industry Keywords
Data StrategyData ModernizationData GovernanceUK GDPRData Quality
Tech Stack
Tools & technologiesApacheAWSAzureCloudETLHadoopKafkaPythonSQL
About the role
Key responsibilities & impact- Advise customers on enterprise data strategy and target-state architecture
- Lead modernization of legacy data warehouses, databases and fragmented data estates
- Design end-to-end lakehouse, warehouse, federated and data-product architectures
- Design ingestion, integration, storage, transformation, governance and semantic-layer solutions
- Ensure platforms provide AI-ready data with appropriate quality, context, access and governance
- Build security, privacy, monitoring, reliability and cost management into architectures
- Provide impartial advice across Databricks, Snowflake, Microsoft Fabric and Starburst
- Qualify opportunities and lead discovery workshops with customers, sales specialists, business development managers and partners
- Maintain accurate CRM information for forecasting and pipeline management
- Run demonstrations and scope proofs of value
- Produce solution designs, HLDs, RFP responses, business cases, TCO/ROI models and technical input to statements of work
- Collaborate with AI, Hybrid Platforms, Cloud, Security and delivery teams for end-to-end solution handover
- Develop reference architectures, modernization assessments, demonstrations and packaged go-to-market offerings
- Engage with data-platform partners and maintain current knowledge of roadmaps and capabilities
- Maintain platform accreditations through lab work, research and training
- Track emerging trends and share practical insights internally and externally
Requirements
What you’ll need- Typically 5+ years in data architecture, data engineering or analytics platforms
- At least 3 years in a customer-facing pre-sales, consulting or solution architecture role
- Proven track record designing enterprise data platforms and explaining architectural trade-offs
- Experience leading data estate modernisation from legacy warehouses, on-premises databases or Hadoop to modern cloud or hybrid platforms
- Deep expertise in at least one of Databricks, Snowflake or Microsoft Fabric, with working knowledge of alternatives
- Understanding of lakehouse, data warehouse, federated/virtualised data, data mesh and data products
- Knowledge of dimensional, medallion and domain-oriented data modelling and semantic layers
- Experience with batch, streaming, CDC, ETL/ELT and orchestration
- Understanding of relational and non-relational databases, open table formats and storage lifecycle design
- Knowledge of data governance, cataloguing, lineage, data quality, ownership, classification and access policy
- Understanding of data security, privacy and compliance requirements, including UK GDPR
- Ability to model consumption-based platform costs and build TCO and ROI cases
- Hands-on SQL and Python ability
- Experience leading discovery workshops and communicating with technical and business stakeholders
- Ability to produce solution designs, proposals and RFP responses
- Degree or equivalent qualification in computer science, data or a related field is desirable; equivalent experience equally valued
- Relevant certifications are desirable
- Experience with Azure, AWS or Google Cloud is desirable
- Knowledge of Apache Kafka or cloud-native streaming equivalents is desirable
- Familiarity with BI and semantic tooling is desirable
- Understanding of data platforms for AI and machine learning is desirable
- Experience with data observability, DataOps, channel/reseller environments, reusable offerings, mentoring or virtual-team leadership is desirable
Benefits
Comp & perks- Hybrid work arrangement
- Professional development, training and continuous learning opportunities
- Relevant accreditation and certification support
- Collaborative coworker environment
- Equal opportunity employment