Data & Analytics Architect

About the role

The Data & Analytics Architect defines and governs enterprise data architecture to enable trusted analytics, reporting, and data‑driven decision‑making across digital platforms and smart solutions. You will work closely with architecture, security, governance, analytics, and delivery teams to create scalable, cloud‑agnostic data platforms.

Key responsibilities

• Define enterprise data platform architecture, including lakehouse, streaming, batch processing, semantic layers, BI platforms, and data product frameworks.
• Establish scalable patterns for data ingestion, transformation, orchestration, and integration across APIs, events, IoT, and enterprise systems.
• Set data modeling standards such as canonical models, dimensional modeling, domain‑driven data products, and master data management practices.
• Design analytics architecture, including semantic models, KPI frameworks, dashboards, and self‑service analytics capabilities.
• Define performance, scalability, availability, partitioning, indexing, and cost‑optimization strategies for data workloads.
• Align data architecture with governance, privacy, and security requirements (classification, lineage, retention, encryption, DLP, access controls).
• Govern integration of vendor‑delivered data platforms, reporting tools, and analytics solutions to ensure consistent data contracts and quality.
• Establish enterprise data quality frameworks with validation rules, monitoring, SLA tracking, issue management, and data health reporting.
• Conduct architecture reviews, validate implementations, and support production readiness, disaster recovery, and operational continuity planning.
• Develop reusable architecture standards, technical documentation, playbooks, and implementation guidelines.

Required profile

• Bachelor’s degree in Compute… (as stated in the posting).
• Strong expertise in modern data architectures, including lakehouse platforms, streaming systems, ELT/ETL pipelines, semantic layers, and data products.
• Experience designing scalable, secure, and cost‑efficient analytics solutions in regulated enterprise environments.
• Proven ability to define enterprise KPIs, metrics governance, and consistent reporting standards.

Required skills

• Lakehouse platforms
• Streaming systems
• ELT/ETL pipelines
• Semantic layers
• Data product development
• Dimensional and domain‑oriented data modeling
• Metadata management and data lineage
• Data quality frameworks
• KPI and metrics governance
• BI platforms and dashboarding tools