Principal Software Engineer, Platform Services

Salesforce

. Design, build, and maintain backend services in Python and Go for Data Platform Services .

Posted 11/11/2026full-timeUnited StatesLead💰 $197,300 - $313,700 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in backend service design and development using Python and Go, with a strong focus on AWS infrastructure management and data platform automation. Proficient in implementing security measures, performance optimization, and maintaining service reliability in production environments.

Highest-signal resume keywords

Expert-Level Python DevelopmentAWS Infrastructure ManagementTerraform Infrastructure as CodeProduction LLM Application EngineeringDistributed Systems Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Backend Service DesignGo DevelopmentSQL and Data ModelingREST and gRPCConcurrency and Asynchronous ProgrammingUnit and Integration TestingOOP PrinciplesCaching and Event-Driven ProcessingPerformance ProfilingAI Evaluation Harnesses

Tools & Technologies

AWS BedrockECS FargateTerraformGitHub ActionsDbtAirflow/MWAASnowflakeDynamoDBOpenTelemetrySlack

Industry Keywords

Data Platform ServicesAI Platform ServicesMulti-Tenant IsolationRBAC EnforcementProduction SystemsEvent-Driven ProcessingGraceful DegradationLatency OptimizationObservabilityModel Routing

Tech Stack

Tools & technologies

AirflowAWSDistributed SystemsDynamoDBGRPCJavaNoSQLPythonSnowflakeSQLTableauTerraformGo

About the role

Key responsibilities & impact

Design, build, and maintain backend services in Python and Go for Data Platform Services
Build Agent runtimes on AWS Bedrock AgentCore/ECS Fargate and event-driven Lambda handlers for AI Platform Services
Design and maintain abstraction layers and custom utilities for data-domain customers
Maintain and service AWS infrastructure using Terraform
Build RAG pipelines and tool-calling AI agents for data products, including retrieval, orchestration, grounding/citations, and evaluation
Integrate model providers with the LLM Gateway using response streaming, prompt caching, structured outputs, and auditable OpenTelemetry
Establish evaluation harnesses, guardrails, and tracing to measure agent quality, latency, and cost and catch regressions
Build and maintain dbt services, MWAA/Airflow orchestration, and data-platform automation
Extend the platform CLI and shared utilities used by data teams
Implement OAuth/OIDC flows, token vaulting, session binding, least-privilege IAM, and secrets management
Enforce multi-tenant isolation and per-user identity/RBAC across services and agents
Own service reliability and delivery, including Terraform, GitHub Actions CI/CD, container builds, logging, metrics, tracing, alerting, and cost controls
Set technical direction through system/API design, code review, and mentoring

Requirements

What you’ll need

15+ years designing, building, and maintaining production backend services at scale
Expert-level Python, Go, or Java for server-side development
Solid grasp of relevant frameworks and the WSGI/ASGI model
Experience with REST and/or gRPC, streaming patterns, pagination, versioning, idempotency, and backward-compatible contracts
SQL, data modeling, query optimization, indexing, transactions, and connection pooling
Experience with Snowflake, DynamoDB, and relational, warehouse, and NoSQL/key-value stores
Knowledge of caching, background jobs/workers, queues, event-driven processing, rate limiting, retries/backoff, and timeouts
Performance profiling, load handling, latency/throughput trade-offs, graceful degradation, and failure-oriented design
Structured logging, metrics, distributed tracing, and live production debugging
OOP required, including encapsulation, abstraction, inheritance, composition, polymorphism, SOLID principles, and design patterns
Strong data structures and algorithms knowledge
Concurrency and asynchronous programming, including async/await, threading, and event loops
Unit, integration, and end-to-end testing; testable design; Git and PR-based workflows
Production AWS experience with ECS/containers, Lambda, IAM, API Gateway, and DynamoDB
Terraform infrastructure as code; GitHub Actions or equivalent CI/CD; container builds
Distributed systems expertise, including scalability, resilience, consistency, retries, idempotency, and partial failure
OAuth/OIDC, authentication/authorization, token handling, least-privilege access, multi-tenant isolation, and secrets management
Production LLM application engineering, including RAG, embeddings, vector search, hybrid search, reranking, grounding/citations, and retrieval evaluation
Agent development, prompt engineering, tool/function calling, structured outputs, orchestration, and prompt caching
Integration with Anthropic/Claude, AWS Bedrock/AgentCore, and Snowflake Cortex
AI evaluation harnesses, guardrails, tracing/observability, and token/latency/cost optimization
AI security, including prompt injection, data exfiltration, PII handling, and per-user identity/RBAC enforcement
Ability to work in San Francisco, California or Seattle, Washington
Preferred: dbt, Airflow/MWAA, Snowflake, MCP, Slack platform, Monte Carlo, Iceberg/lakehouse patterns, Tableau/BI integration, fine-tuning, semantic caching, model routing/fallback, or AI capabilities in production systems

Benefits

Comp & perks

Time off programs
Medical insurance
Dental insurance
Vision insurance
Mental health support
Paid parental leave
Life insurance
Disability insurance
401(k)
Employee stock purchasing program
Potential incentive compensation, equity, and benefits, as applicable
Reasonable accommodation during the application or recruiting process