Principal Software Developer – Data Architect

What you will be doing:

• Lead enterprise data platform architecture and modernization: Define and execute the technical 
strategy for a scalable, AI-Ready, enterprise data platform, including Sherlock modernization, 
lakehouse architecture, data products, interoperability, and the patterns and capabilities needed to 
support AI-Ready use cases.
• Establish data architecture patterns: Create and evolve reference architectures, modeling 
standards, guardrails, and best practices for our foundational data platform, including Icebergbased lakehouse architecture, medallion patterns, ingestion, normalization, data quality, and 
interoperability.
• Use and mentor teams on AI-assisted workflows: Apply AI tools in daily architecture, analysis, 
documentation, and prototyping, and mentor teams in responsible usage that improves design 
quality, data discovery, and delivery effectiveness.
• Oversee key platform projects: Contribute heavily to AI-Ready data platform initiatives and crossproduct data architecture improvements, including data layer re-architecture for our SE and 
Sherlock products, schema modernization, and data model evolution.
• Mentor and lead: Guide teams in delivering projects, fostering a mentorship culture, and ensuring 
adherence to high standards in data engineering practices, data modeling, data quality, and 
platform architecture.
• Drive best practices: Collaborate with R&D groups to implement best practices for making trusted, 
AI-Ready, and securely interoperable data proucts, including data contracts, ingestion and 
normalization standards, and improving consistency and reuse across products
• Partner on data governance and security: Work with Security and product teams to define data 
classification, retention, tenant isolation, and access controls for datasets and data products.
• Enable adoption through paved roads: Provide reference implementations and blueprints that 
make it easy for teams to produce data products and integrate with the data platform.
• Architect for data observability: Define and implement standards for data quality, lineage and 
traceability, data dictionary controls, freshness monitoring, and alerting, so data products are 
reliable and audit-ready.

What you will bring:

• 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.

Technologies you’ll work with:

• Core (current): AWS S3, S3 Express, DynamoDB, Athena, Glue Catalog, Lake Formation, 
OpenSearch Serverless, S3 Vector Storage, EMR/EMR Serverless, Spark, Trino, MapReduce,
Iceberg, Lambda, Step Functions, EKS, SNS/SQS; MongoDB, Amazon DocumentDB, MS SQL 
Server, Redis/Valkey; Java (Spring), Python.
• AI -ready data patterns and tooling: AWS Bedrock (including models such as Anthropic Claude), 
AWS Knowledge Bases, MCP, embeddings, vector retrieval, and RAG.
• Observability & operations: CloudWatch, New Relic, OpenTelemetry.
• Emerging: Kafka or Pub/Sub, LLM proxy layer (e.g. LLMProxy), Aurora PostgreSQL, pgvector