Principal Software Developer – Data Architect

• 10+ years of experience in software development and data engineering, with at least 5 years in a senior technical leadership role, preferably as a Principal Developer or Data Architect. • Deep experience designing modern data platforms on AWS cloud-native infrastructure, including lakehouse, medallion, and analytics patterns, ingestion from OLTP systems, ETL/ELT pipelines, distributed processing with Spark, Trino, and delivering analytics and AI-Ready data lakes at scale, with strong operational practices. • Practical, hands-on use of AI tools to improve data architecture and engineering workflows, including analysis, design exploration, documentation, prototyping, code assistance, and mentoring teams on responsible, effective usage. • Hands-on experience with core data technologies and integration patterns: MongoDB, Amazon DocumentDB, MS SQL Server, DynamoDB, AWS ElastiCache for Redis, and Valkey; event streaming and queueing using SNS/SQS. Postgres, pgvector, and Kafka or Pub/Sub are an asset. • Hands-on experience with AWS data platform services: S3, S3 Express, Athena, Glue Catalog, Lake Formation, OpenSearch Serverless, S3 Vector Storage, Iceberg, Lambda, Step Functions, EKS, ETL on EMR, and EMR Serverless. • Proven ability to architect and deliver scalable, reliable data systems and product data architectures, guiding teams in data models, storage and integration architectures, data contracts, data domain taxonomy, schema and event versioning, and resolving performance and scale bottlenecks. • Proficiency in data movement and performance architecture: Experience designing replication, event sourcing, and CDC/change tracking strategies, safe historical reprocessing patterns, and performance optimization through query analysis, indexing, and partitioning. • Experience defining data governance and platform adoption standards in large organizations, including controls for privacy, access, auditability, safe reuse, and operational guardrails for AI-Ready datasets and data products. • Experience enabling secure interoperability patterns with customer systems and AI workflows, including governed data access, tenant-aware controls, and safe integration patterns. • Familiarity wth AI-ready data patterns is preferred, including embedding pipelines, vector-based retrieval, RAG data workflows, and real-time/event-driven data flows that support AI integrations. • Practical familiarity with AI platform integration concepts such as MCP, AWS Bedrock, AWS Knowledge Bases, vector retrieval, and RAG workflows is preferred. • Strong technical leadership: Experience mentoring teams, setting engineering and architecture standards, and influencing technical direction across multiple teams. • Experience working with DevOps teams, CI/CD pipelines, infrastructure-as-code, and operational tooling to deliver scalable, resilient data platforms and pipelines. • Communication and collaboration skills to align cross-functional teams and engage with senior leadership on technical strategy, trade-offs, and decisions.

Key Success Factors:

• Establish a solid technical strategy: Collaborate with data platform, product, and architecture leadership to define the AI-Ready Data Platform’s technical direction, ensuring alignment with business growth, scalability, and interoperability objectives. • Deliver architecture patterns and standards: Define, prototype, and socialize key data architecture patterns and modeling standards backed by reference documentation and architecture decision records that teams can apply consistently. • Advance key platform initiatives: Contribute significantly to AI-Ready Data Platform initiatives and cross-product data architecture improvements, strengthening the foundation for AI capabilities, interoperability, scalability, and performance. • Mentor and guide teams: Cultivate high-performing development teams, driving adoption of best practices in data modeling, data quality, governance, and operational excellence.