Data Architect

Proven ability to design scalable architectures using partitioning, sharding, replication, workload isolation, horizontal scaling, and distributed processing patterns

Experience architecting batch, streaming, event-driven, real-time, near-real-time, and API-based data integration patterns

Strong understanding of how application workloads, analytical workloads, reporting workloads, workflow engines, and AI/ML workloads interact with enterprise data platforms

Hands-on capability with technologies such as SQL, Python, Java, Spark, Kafka, Airflow, APIs, and modern data pipeline frameworks

Expertise in data modeling, including conceptual, logical, physical, dimensional, canonical, domain-driven, and event-based models

Strong knowledge of data security architecture, including encryption, tokenization, masking, access controls, entitlement models, secrets management, and audit logging

Ability to define and evaluate non-functional technical requirements, including latency, throughput, scalability, availability, resiliency, recovery, observability, security, and maintainability

Familiarity with designing data architectures that support AI, machine learning, feature engineering, model training, retrieval-augmented generation, vector search, and analytical workloads