Data Architect

Define and maintain enterprise data architecture, including conceptual, logical, and physical data models. Design scalable cloud-based data platforms, data warehouses, and data lake solutions to support business intelligence, analytics, and AI initiatives. Lead the design and implementation of data integration, ETL/ELT, streaming, and batch processing solutions. Partner with business stakeholders, product managers, architects, and engineering teams to translate business requirements into scalable technical solutions. Ensure data platforms are reliable, performant, secure, and highly available through monitoring, automation, and operational best practices. Evaluate emerging technologies and recommend improvements to modernize the enterprise data ecosystem. Collaborate with cross-functional teams to support AI/ML, advanced analytics, and enterprise reporting initiatives.

Qualifications Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field. 10+ years of experience in data architecture, data engineering, or enterprise data platform development. 5+ years of experience leading data engineering projects and delivering enterprise-scale data solutions. Strong expertise in data modeling, data warehousing, ETL/ELT, and cloud-based data platforms. Experience designing distributed, scalable, and high-performance data architectures. Strong understanding of Agile development methodologies, CI/CD, and DevOps practices. Excellent communication, stakeholder management, and leadership skills. Preferred Technical Skills Cloud & Data Platforms: AWS, Snowflake, S3, EMR, Glue, Redshift Data Engineering: Spark, Kafka, Airflow, Talend, dbt, Python, SQL Databases: SQL Server, PostgreSQL, Aurora, DynamoDB Architecture: Data Lake/Lakehouse, Event-Driven Architecture, Microservices, REST APIs, Data Modeling DevOps: Kubernetes, Docker, Git, CI/CD, Infrastructure as Code