Data Architect

Only candidates local to Dunwoody and surrounding areas will be considered

REQUIRED EXPERIENCE

Databricks experience (required) strong working proficiency with 2 4+ years strongly preferred. Deep AWS data ecosystem experience , including design and hands-on engineering with structured, unstructured, relational, and NoSQL data. Database expertise in Oracle and PostgreSQL including schema design, indexing, and performance tuning. Strong data modeling skills; ability to design optimal models and produce professional ER diagrams. Near-expert experience optimizing databases for performance and scalability. Strong understanding of multi-tenant architectures , including schema isolation and workload management. Experience with both OLTP and OLAP systems, including modern data warehousing and ETL/ELT patterns. Experience with CDC, data streaming, and queuing systems (e.g., Kafka, Kinesis, Pulsar). Ability to architect data sharing solutions enabling customers to ingest data in their own data lakes. Experience designing or supporting data pipelines optimized for AI/ML workloads, including feature engineering, vector storage, model-ready datasets, or integration with AI platforms (e.g., SageMaker, Databricks ML, or similar).

PREFERRED QUALIFICATIONS

Experience building data lakes that support AI or advanced analytics. Experience designing data architectures that feed or support AI/ML models. Previous ownership of enterprise data strategy or acting as the primary data expert within an organization. Bachelors in Computer Science or related IT degree, preferred Master's Degree

KEY RESPONSIBILITIES

Architect, design, and optimize enterprise data solutions across AWS environments (structured, unstructured, relational, and NoSQL sources). Design logical and physical data models, including ER diagrams, dimensional models, and multi-tenant schemas. Lead performance tuning and optimization efforts for Oracle and PostgreSQL databases. Define and implement OLTP and OLAP architectures, including data warehousing and ETL/ELT best practices. Build and support real-time data streaming solutions (including CDC, queuing technologies like Kafka, and event-driven pipelines). Enable near real-time delivery of data changes directly to customers for consumption within their own data lakes. Architect and grow modern data lake and environments, ideally leveraging Databricks as the strategic platform. Collaborate with engineering teams and offshore resources to deliver scalable, reliable, and well-governed data systems. Establish standards, best practices, and architectural patterns for enterprise data management. (Preferred) Build or enhance data platforms that support AI/ML workloads, including data readiness, optimization, and pipelines feeding AI models