Data Architect

Job Description.

• Modernize, optimize and evolve a cloud-native data stack into a scalable, secure and high performing ecosystem such as SQL & NoSQL repositories for diverse workloads
• Leverage modern data platform principles including lakehouse, data fabric and data mesh architectures to improve scalability, decentralization, governance, and interoperability.
• Design Scalable Architectures: Build flexible architectures to support analytics, real-time reporting, AI/ML Ops pipelines, feature stores, and graph-based data relationships.
• Unified Data Platform: Design high performing and cost effective solutions that handle both structured and unstructured data at high volume and high velocity.
• Establish Modeling Standards: Define enterprise-wide standards for data modeling, semantic layers, and golden datasets to enable consistency and reuse.
• Advance Governance & Quality: Implement frameworks for data lineage, quality, privacy, and security (GDPR, CCPA, PII protections).
• Enable Personalization & Insights: Architect data capabilities that fuel advanced recommendations, relationship mapping, and connected user experiences.
• Evaluate & Integrate Technologies: Guide adoption of best-fit technologies to support evolving use cases in data science, AI, and scalable analytics.
• Partner Cross-Functionally: Collaborate with engineering, product, analytics, and data science teams to translate business vision into technical solutions.
• Mentor & Lead: Provide architectural leadership, coaching engineers on best practices while influencing strategic platform decisions..

Required Skills:

• 10+ years of experience in data architecture, platform engineering, or data engineering, with at least 3+ in a senior or architect role.
• Proven expertise with modern cloud-native data stacks including Snowflake, Databricks, MDM, Data Catalogs and CDPs.
• Hands-on experience with Looker or other BI/semantic modeling tools.
• Strong understanding of graph-based data models, recommendation systems, and personalization architectures.
• Expertise in data modeling, metadata management, and semantic layers.
• Proficiency with streaming/event-driven data pipelines and real-time processing (Kafka, Pub/Sub, Spark Streaming, etc.).
• Familiarity with AI/ML pipelines, feature stores, and unstructured/structured data integration.
• Strong foundation in data governance, lineage, cataloging, and compliance frameworks.
• Excellent communication and leadership skills to align technical direction across diverse teams