Principal Software Engineer, Platform Services

Quick Facts

• Backend-heavy role building platform services, APIs, and automation for data products.
• Includes production AI/agent systems, RAG pipelines, and AWS-based delivery.

Description

Design, build, and maintain the backend services, APIs, data-platform automation, and AI platform/agent systems that support Salesforce data products. The work spans distributed Python services and streaming, AWS LLM/agent runtimes, RAG pipelines, and dbt/Airflow automation powering a Snowflake-based lakehouse.

Responsibilities

• Design, build, and maintain backend services in Python/Go to support data platform services, including AI agent runtimes on AWS Bedrock AgentCore/ECS Fargate and event-driven Lambda handlers.
• Maintain abstraction layers and utilities for data platform customers.
• Use Terraform to maintain infrastructure for AWS accounts.
• Build RAG pipelines and tool-calling AI agents (retrieval, orchestration, grounding/citations, evaluation).
• Integrate model providers with an LLM Gateway featuring streaming, prompt caching, and structured auditable outputs (OTeL).
• Stand up eval harnesses, guardrails, and tracing to measure quality/latency/cost and catch regressions before release.
• Develop data-platform automation with dbt services and Airflow (MWAA) orchestration so data products are discoverable, governed, and consumable.
• Extend a platform CLI and reusable shared utilities used by data teams.
• Implement OAuth/OIDC-based identity and security, least-privilege IAM, secrets management, and multi-tenant isolation with per-user RBAC.
• Own reliability and delivery using Terraform, GitHub Actions CI/CD, container builds, structured logging, metrics/tracing, alerting, and cost controls.
• Set technical direction through system/API design, code review, and mentoring.

Requirements

• 15+ years building and maintaining production backend services at scale.
• Expert-level Python/Go/Java for server-side development.
• Strong REST and/or gRPC service and API design, including streaming, pagination, versioning, idempotency, and backward-compatible contracts.
• Solid SQL and data modeling knowledge with query optimization and indexing; experience across relational/warehouse and NoSQL stores.
• Experience with caching strategies, background workers/queues, event-driven processing, rate limiting, retries/backoff, and timeouts.
• Performance and reliability expertise (profiling, load handling, latency/throughput trade-offs, graceful degradation, failure design).
• Observability expertise: structured logging, metrics, distributed tracing.
• Strong software engineering fundamentals: OOP, SOLID, design patterns, data structures/algorithms, and concurrency/async.
• Testing experience across unit/integration/end-to-end and testable design; Git and PR-based workflows with disciplined code review.
• Production AWS experience: ECS/containers, Lambda, IAM, API Gateway, DynamoDB.
• Infrastructure as code with Terraform and CI/CD with GitHub Actions (or equivalent).
• Distributed systems expertise for scalable, resilient, loosely coupled services and partial-failure handling.
• Security expertise including OAuth/OIDC, authn/authz, least-privilege, multi-tenant isolation, and secrets management.
• Production-grade LLM engineering: RAG and agents/tool use with structured outputs.
• Experience integrating model providers via an LLM Gateway with response streaming; ability to implement eval harnesses, guardrails, and tracing.
• AI security: prompt injection, data exfiltration, PII handling, and per-user identity/RBAC enforcement.

Benefits

• Time off programs; medical, dental, vision; mental health support.
• Paid parental leave; life and disability insurance.
• 401(k) and employee stock purchasing program.