Data Architect

Requirements:

• Strong architecture and system-design mindset with the ability to balance long-term strategy and immediate business needs.
• Ability to simplify complex data problems and design scalable, pragmatic solutions.
• Strong ownership and ability to influence technical decisions across teams.
• A hands-on technology leader who can design, review, and guide implementation rather than working purely at a conceptual level.
• Strong focus on engineering excellence, reliability, security, and measurable business impact.
• 13-15 years of overall experience in data engineering, data architecture, or related areas, with significant experience in architecture and technical leadership.
• Strong experience designing large-scale, distributed data platforms and enterprise data architectures.
• Strong understanding of data modeling, data warehousing, data lakes, lakehouse architecture, and distributed data processing.
• Hands-on experience with technologies such as Spark, Kafka, Airflow, Databricks, Snowflake, BigQuery, Redshift, or equivalent platforms.
• Strong programming experience in Python, Java, or Scala.
• Strong knowledge of SQL and database technologies, including relational and NoSQL databases.
• Experience with cloud platforms, preferably AWS, with a strong understanding of cloud-native data services.
• Experience designing real-time and batch data processing architectures.
• Strong understanding of microservices, APIs, distributed systems, and event-driven architectures.
• Experience with data governance, security, quality, lineage, metadata, and compliance.
• Strong understanding of data architecture patterns and best practices for scalability, availability, performance, and cost optimization.
• Experience working with CI/CD, DevOps, infrastructure automation, and observability for data platforms.
• Excellent problem-solving, communication, stakeholder management, and technical leadership skills.

Good to Have:

• Experience architecting data platforms for AI/ML and generative AI applications.
• Knowledge of vector databases, RAG architectures, knowledge graphs, and AI data platforms.
• Experience with Data Mesh, Data Fabric, or modern lakehouse architectures.
• Experience with technologies such as Delta Lake, Iceberg, dbt, Kubernetes, Terraform, or equivalent.
• Experience in building enterprise-scale platforms in a high-growth product/SaaS environment.
• Prior experience leading architecture initiatives across multiple engineering teams.