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.