Data & Analytics Architect

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. The role designs scalable and cloud-agnostic data platform architectures, ensuring interoperability, governance, security, and high-quality data delivery across multiple systems and vendors.

Working closely with architecture, security, governance, analytics, and delivery teams, the architect establishes reference architectures, data modeling standards, and analytics frameworks while ensuring performance, scalability, reliability, and operational supportability of data solutions.

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.
• Define data modeling standards including 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 including classification, lineage, retention, encryption, DLP, and access controls.
• Define reference architectures for operational analytics, near-real-time reporting, and event-driven analytics solutions.
• Govern integration of vendor-delivered data platforms, reporting tools, and analytics solutions to ensure consistent data contracts and quality standards.
• Establish enterprise data quality frameworks including 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 Skills & Competencies

• Strong expertise in modern data architectures including lakehouse platforms, streaming systems, ELT/ETL pipelines, semantic layers, and data products.
• Strong understanding of scalable, secure, and cost-efficient analytics solutions within enterprise and regulated environments.
• Expertise in dimensional and domain-oriented data modeling, metadata management, lineage, and data quality practices.
• Experience defining enterprise KPIs, metrics governance, and consistent reporting standards across stakeholders.
• Strong analytical, problem-solving, documentation, and stakeholder communication skills.
• Ability to align technical and business teams on enterprise data standards and governance practices.

Qualifications & Experience

• Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or related field. Master’s degree preferred.
• 8+ years of experience in data architecture, analytics engineering, BI platforms, or enterprise data management.
• Experience delivering enterprise data platforms across cloud and hybrid environments.
• Hands-on experience with streaming/event-driven data architectures and orchestration frameworks.
• Experience with enterprise BI platforms, semantic layers, and large-scale reporting environments.
• Familiarity with governance, auditability, retention, privacy, and compliance requirements in regulated industries.

Preferred Technologies & Tools

• Data Platforms: Databricks, Spark, Delta Lake, Iceberg, object storage platforms
• Streaming & Orchestration: Kafka, Event Hubs, Airflow, Azure Data Factory, dbt
• Analytics & BI: Power BI, Tableau, Looker, semantic models, SQL engines
• Cloud & Data Services: Azure, AWS, BigQuery, Synapse, Trino

Soft Skills

• Strong stakeholder alignment and collaboration capabilities across business, technical, and vendor teams.
• Excellent communication skills with the ability to explain complex data concepts to non-technical audiences.
• Strong analytical thinking and structured problem-solving abilities.
• Ability to balance scalability, performance, governance, and cost considerations effectively.
• Proactive, detail-oriented, and delivery-focused mindset.