Lead Software Engineer - Lead Data Architect

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Bachelor’s degree or equivalent in computer science/information technology or software engineering concepts fields and 10+ years applied experience spanning data architecture and software development.
• Deep expertise in data architecture and database technologies across relational (Oracle, PostgreSQL) and NoSQL (Cassandra, MongoDB), including data modeling, schema design, indexing strategy, replication, sharding/partitioning, and query optimization at scale.
• Proven experience designing large-scale data platforms — data pipelines, streaming/ingestion, warehousing/lakehouse patterns, and data governance, lineage, and quality.
• Working knowledge in Java (ideally Java 17+), or any other language and experience with microservices and event-driven architectures, including API design (REST, gRPC, GraphQL) and messaging/streaming such as Kafka.
• Demonstrable experience with AWS — designing, deploying, and operating production data and application workloads at scale, with clear ownership of reliability and cost outcomes.
• Hands-on experience integrating AI/ML and GenAI into enterprise applications, including LLMs, RAG pipelines, embeddings, vector stores/databases, model evaluation, and production monitoring (MLOps).
• Demonstrated experience leading effective use of approved AI-assisted software development tools (coding, code review, test acceleration, troubleshooting), with the ability to set team expectations for validating AI outputs for correctness, performance, and security. Strong design and architectural thinking — making trade-offs explicit and balancing speed, risk, maintainability, and total cost of ownership across data and distributed systems.
• Good grasp of DevOps and platform/data engineering practices: CI/CD, Infrastructure as Code (IaC) and observability tooling (logs, metrics, traces).
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices