Principal Software Engineer, Platform Services (PMTS)
About the role
backend-heavy
discoverable, governed, and consumable data products
quickly and safely.
About the platform you'll work on
- Lakehouse & warehouse
- Snowflake with Apache
Iceberg
medallion architecture
Transformation
dbt
Orchestration
Airflow on AWS MWAA
Developer tooling
- an internal
CLI
Data quality & governance
Monte Carlo
AI layer
- Agent/MCP/Skill deployment framework, RAG, semantic router / broker agents, over our data products and catalog metadata; conversational agents in Slack;
model access via AWS Bedrock / AgentCore
Snowflake Cortex
Cloud & delivery
- AWS (
ECS Fargate, Lambda, API Gateway, DynamoDB, S3, IAM, etc.
Terraform
GitHub Actions
Responsibilities
- Backend services
- Design, build, and maintain backend services in Python / Go to support our Data Platform Services - Agent runtimes on
AWS Bedrock AgentCore / ECS Fargate
Lambda
handlers for our AI Platform Services
- Design and maintain our abstraction layers for for our data domain customers via custom developed utilities
- Maintain and service our infrastructure using IaC (Terraform) for our AWS accounts
•
AI / agents
- Build
RAG
tool-calling
AI agents for our data products: retrieval, orchestration, grounding/citations, and evaluation.
- Integrate model providers with our LLM Gateway with response streaming, prompt caching, and structured outputs in an auditable way (OTeL)
- Stand up
eval harnesses, guardrails, and tracing
Data-platform automation
- Build and maintain data-platform automation:
dbt
MWAA/Airflow
orchestration, and tooling that makes data products discoverable, governed, and consumable.
- Extend the platform
CLI
Identity & security
- Implement auth and identity:
OAuth/OIDC
IAM
, and secrets management.
- Enforce
multi-tenant isolation
Reliability & delivery
- Own service reliability and delivery:
Terraform
GitHub Actions
CI/CD, container builds, structured logging, metrics/tracing, alerting, and cost controls.
- Set technical direction: system and API design, code review, and mentoring.
Required skills
Backend engineering
- 15+ years
designing, building, and maintaining production backend services at scale •
Expert-level Python / Go / Java
WSGI/ASGI
Service & API design
Data layer
Snowflake
DynamoDB
Server-side patterns
Performance & reliability
Observability
structured logging, metrics, distributed tracing, and debugging live production issues.
Software engineering fundamentals
- OOP (required):
encapsulation, abstraction, inheritance, composition, polymorphism;
SOLID
principles; design patterns applied pragmatically; strong domain modeling.
- Solid
data structures & algorithms
Concurrency & async
Testing
Git
and PR-based workflows; disciplined code review.
Cloud & infrastructure
- Production AWS
: ECS/containers, Lambda, IAM, API Gateway, DynamoDB.
- Infrastructure as code with
Terraform
GitHub Actions
or equivalent) and container builds.
Distributed systems
- Building services that are horizontally scalable, resilient, and loosely coupled; handling consistency, retries, idempotency, and partial failure.
Security
- OAuth/OIDC
, authn/authz, token handling, least-privilege access, multi-tenant isolation, secrets management.
AI / LLM engineering
- Building LLM applications in production
(not research). •
RAG
Agents
prompt engineering, tool use/function calling, structured outputs, single- and multi-step orchestration, prompt caching.
- Integration with model providers -
Anthropic/Claude, AWS Bedrock/AgentCore, Snowflake Cortex
- and response streaming.
•
AI quality & ops
AI security
Nice to have
- dbt, Airflow/MWAA, Snowflake
hands-on experience. •
MCP (Model Context Protocol)
Slack platform
(Bolt, Socket Mode, Block Kit) or other real-time/conversational backends.
- Data observability (
Monte Carlo
) or data-quality/governance tooling.
- Fine-tuning/adaptation, semantic caching, or model routing/fallback.
- Experience
adding AI capabilities to existing production systems
Iceberg
Tableau
Not included in the source posting: about the role, benefits.
Skills
Who can apply
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