Data & Analytics Architect

Overview

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.