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