Data Architect (Data Modernization)

Job Responsibilities

• Assess and document the current enterprise data architecture, including ingestion, processing, storage, and consumption layers across cloud and hybrid systems.
• Lead the review of data pipelines and infrastructure, identifying opportunities to optimize scalability, reliability, performance, and cost-efficiency.
• Design and recommend target-state architecture aligned to modernization objectives, including support for data products, domain ownership models, and AI-readiness.
• Evaluate and improve monitoring, observability, and lineage capabilities, ensuring system transparency and operational resilience.
• Guide the design and enhancement of metadata and governance frameworks, including cataloging, data stewardship, access control, and compliance alignment.
• Collaborate with cross-functional teams to assess and integrate the existing analytics tooling landscape into the broader architecture vision.
• Provide architectural guidance to support agentic BI and AI-driven analytics initiatives, ensuring foundational readiness across data layers.
• Translate assessment findings into strategic architecture roadmaps and reference models, enabling phased modernization.
• Partner with the Technical Product Manager and Engineering leads to align architecture with business goals and operational realities.

Basic Qualifications

• 5 - 7 years of experience in data architecture or enterprise architecture roles, with a strong foundation in designing and modernizing large-scale data systems.
• Proven experience architecting solutions on cloud platforms such as AWS, Azure, or GCP using modern components (e.g., Lakehouse, Delta Lake, Redshift, BigQuery, Snowflake, etc.).
• Expertise in data modeling, data integration frameworks, and real-time/batch processing architecture (e.g., Spark, Kafka, dbt, etc.).
• Solid understanding of data governance, metadata management, and security practices, including experience with tools like Collibra, Alation, or AWS Glue Data Catalog.
• Familiarity with observability frameworks, system health monitoring, and end-to-end data lineage design.
• Experience evaluating or designing support for advanced analytics, AI/ML pipelines, and agentic BI solutions.
• Strong communication and stakeholder engagement skills, capable of working with executives, engineers, and analysts to bridge business and technical needs.

Preferred Qualifications

• Experience working in regulated enterprise environments (e.g., finance, healthcare, energy).
• Exposure to agentic AI systems, LLM integrations, or modern autonomous analytics platforms.
• Certifications in cloud architecture (e.g., AWS Certified Data Analytics – Specialty, Azure Data Engineer Associate, Google Professional Data Engineer).