Senior Integration Platform Engineer (Sales Team)

We are looking for a Senior Integration Platform Engineer to own and evolve the core integration and data backbone that underpins GTM Analytics, Enterprise-grade AI tooling, and Business-critical data flows across Semrush. This is a global, high-trust engineering role with real ownership. You will design and operate secure, scalable cloud services and data integrations used across regions and teams, including regulated, SOX-relevant data flows (e.g. global compensation feeds). You will also play a key role in supporting post-acquisition integration work with Adobe.This role partners closely with Analytics, Go-To-Market Engineering, Security, and Finance - without owning GTM systems directly. What you’ll do Design, build, and operate production APIs and cloud services (REST and/or gRPC) on Google Cloud Platform Own Cloud Deployed Solutions/Scripts (Cloud Run, Docker etc..) that power integration, provisioning, and internal enablement platforms Build and maintain secure, event-driven integration patterns (Pub/Sub, async workflows) Own business-critical data integrations, including global compensation and finance-adjacent feeds with SOX relevance Design and enforce security and access boundaries (IAM, secrets, service-to-service auth, cloud ↔ on-prem connectivity) Drive cost-efficient cloud execution, including batching, async processing, and pricing-aware architecture decisions Build and operate enterprise-grade AI services, with clear cost, latency, and quality trade-offs Design reliable data ingestion patterns that support analytics, executive reporting, and downstream consumers Act as primary owner for integration infrastructure and enterprise AI tooling for the RevOps team Act as secondary owner for analytics pipelines to ensure coverage and eliminate single points of failure Lead operational ownership: monitoring, alerting, incident response, root-cause analysis, and audit readiness How you’ll collaborate Partner with Analytics Engineering and Analytics teams to ensure stable, well-governed data flows Enable Go-To-Market Engineering teams through reliable APIs, integrations, and automation primitives (without owning GTM systems) Work with Security, Finance, and Legal stakeholders on access control, auditability, and compliance-relevant systems Support global teams across regions and time zones What we’re looking for Core engineering experience Senior backend / platform engineer with strong systems thinking Proven experience owning production, business-critical systems end-to-end Strong Python, Go, or JavaScript engineering background Hands-on experience building and operating production data warehouse tables (BigQuery or similar), with strong SQL and a focus on reliability, performance, and AI-ready data design API design experience with attention to contracts, versioning, and backward compatibility Experience with event-driven and asynchronous architectures   Data engineering fundamentals (required) Strong grounding in data engineering principles, including: schema evolution and data contracts idempotent ingestion, replayability, and backfills batching and late-arriving data protecting downstream analytics and reporting consumers Experience operating data pipelines that support executive, financial, or compensation reporting Comfort working in environments with auditability, controls, and change discipline (SOX familiarity is a plus)   GCP & infrastructure Hands-on experience with GCP (Cloud Run, Cloud Functions, Pub/Sub) Hands-on experience with Google Cloud Container tools (Cloud Run, GKE, Artifact Registry, Docker) Experience with IAM, least-privilege access, and secrets management Infrastructure-as-code (Terraform or equivalent) Observability: logs, metrics, alerts, SLIs/SLOs Experience with orchestration tools such as Apache Airflow is a plus.   AI-native engineering (must-have) Native, daily use of AI coding tools such as Claude Code, Codex, Cursor, or equivalent Experience applying AI tools to: production code development and refactoring debugging and incident analysis architectural trade-off evaluation   Experience assessing LLM cost economics, including: model selection trade-offs (latency, quality, cost) batching and token-efficiency strategies build vs buy decisions for AI-powered workflows Proven judgment shipping LLM-powered functionality in production-safe, cost-aware systems Business context & judgment (required) Demonstrated ability to reason about business impact, not just technical correctness Experience working with or adjacent to Sales, RevOps, or Finance teams, where data quality or availability affected revenue, incentives, or compensation Comfort translating business requirements into robust, auditable technical systems Strong judgment balancing speed, cost, risk, and correctness What success looks like Integration and analytics-enabling systems are stable, secure, and auditable Enterprise AI tooling is production-ready and cost-controlled Analytics and partner teams move faster without platform fragility No single-point-of-failure dependencies exist Adobe integration progresses without disruption to core analytics or business reporting   What this role is not Not an owner of Go-To-Market systems Not a pure analytics or SQL/dbt modeling role Not a DevOps-only or infra-only position   This is a senior, high-ownership integration and platform role with global scope and clear business impact.