Lakehouse & warehouse — Snowflake with Apache Iceberg tables in a medallion architecture (Bronze → Silver → Gold → Semantic Views).
Transformation — dbt (Cloud on Fusion) with data contracts, tag-driven governance, and automated project-evaluation checks.
Orchestration — Airflow on AWS MWAA; DAGs and schedules authored and deployed through platform tooling and CI (metadata driven).
Developer tooling — an internal CLI and reusable utilities (dbt, Iceberg operations, Snowflake load, REST API based ingestions, etc.) that abstract the platform for data teams.
Data quality & governance — Monte Carlo for data observability (monitors as code) and centralized governance (tagging, lineage, PII classification, cataloging).
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, centralized LLM Gateway, and Snowflake Cortex.
Cloud & delivery — AWS (ECS Fargate, Lambda, API Gateway, DynamoDB, S3, IAM, etc.), Terraform IaC, GitHub Actions CI/CD, containerized builds, structured logging, metrics, and tracing.
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, and event-driven 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 pipelines and 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 so agent quality, latency, and cost are measurable and regressions are caught before release.
Data-platform automation
Build and maintain data-platform automation: dbt services, MWAA/Airflow orchestration, and tooling that makes data products discoverable, governed, and consumable.
Extend the platform CLI and shared utilities that data teams use as their day-to-day interface to the platform.
Identity & security
Implement auth and identity: OAuth/OIDC flows (per-user 3LO, token vaulting, session binding), least-privilege IAM, and secrets management.
Enforce multi-tenant isolation and per-user identity/RBAC across services and agents.
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 for server-side development; solid grasp of relevant frameworks and the WSGI/ASGI model.
Service & API design: REST (and/or gRPC), request/response and streaming patterns, pagination, versioning, idempotency, and backward-compatible contracts.
Data layer: SQL and data modeling, query optimization and indexing, transactions, connection pooling; relational, warehouse (Snowflake), and NoSQL/key-value (DynamoDB) stores.
Server-side patterns: caching strategies, background jobs/workers, queues and event-driven processing, rate limiting, retries/backoff, and timeouts.
Performance & reliability: profiling, load handling, latency/throughput trade-offs, graceful degradation, and designing for failure.
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; ability to reason about time/space complexity.
Concurrency & async programming (async/await, threading, event loops) and their failure modes.
Testing (unit, integration, end-to-end) and testable design; Git and PR-based workflows; disciplined code review.
Cloud & infrastructure
Production AWS: ECS/containers, Lambda, IAM, API Gateway, DynamoDB.
Infrastructure as code with Terraform; CI/CD (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: chunking, embeddings, vector search, hybrid search, reranking, grounding/citations, context-window management, retrieval evaluation.
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: eval harnesses, guardrails, tracing/observability, token/latency/cost optimization.
AI security: prompt injection, data exfiltration, PII handling, per-user identity/RBAC enforcement.
Nice to have
dbt, Airflow/MWAA, Snowflake hands-on experience.
MCP (Model Context Protocol) and/or applied RAG.
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 / open table formats and lakehouse patterns.
Tableau or other BI integration.
Unleash Your Potential
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In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.

The typical base salary range for this position is $197,300 - $313,700 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $237,700 - $344,700 annually.

The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
