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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Backend Engineer - **Company:** Publicis Groupe - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $191,763.0 - $216,684.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Big Data, Software as a Service, Cloud Computing, Code Review, Continuous Integration, Data Architecture, Data Governance, Data Infrastructure, Data Security, Software Debugging, Distributed Systems, Amazon DynamoDB, Identity and Access Management, Python (Programming Language), Key Management, Performance Tuning, SQL Databases, Data Ingestion, Large Language Models, Core Api, Backend, Cloudformation, Data Lakes, Pyspark, Data Lineage, Production Code, Data Management, Functional Programming, Api Design, Api Gateway, Data Pipelines, Api Management, Serverless Computing, Databricks - **Published:** July 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=be6ffa886ebd632a ## About the Role * 10+ years of backend, data platform, or distributed systems engineering experience, including principal-level ownership across multiple services or teams. * Expertise in Python backend engineering, API design, service boundaries, async/concurrent systems, SQL, and modern testing practices. * Hands-on experience with Databricks, PySpark, lakehouse patterns, and medallion-style data architecture, or equivalent large-scale data pipeline experience. * Strong AWS serverless experience, including Lambda, API Gateway, S3, DynamoDB, IAM, CloudFormation/SAM or equivalent infrastructure-as-code tooling. * Deep understanding of multi-tenant SaaS design, authentication/authorization, tenant-scoped data access, secrets management, and operational security. * Experience designing stable data contracts, CLI/API/MCP contracts, schema evolution patterns, and consumer-facing data models. * Proven technical leadership through mentoring, architecture decisions, code review, trade-off communication, and engineering standards without relying on direct authority. Nice to Have * Experience with marketing, advertising, or platform APIs and similar ecosystems. * Experience with Fivetran, custom connector development, connector reconciliation, or third-party data ingestion operations. * Experience with Delta Lake, Unity Catalog, data governance, data lineage, and lakehouse performance optimization. * Familiarity with AI/LLM systems, agentic workflows, MCP-style tool integration, or similar runtime/tooling architectures. Core Competencies * Technical Depth: You reason across backend services, data pipelines, cloud infrastructure, API contracts, and operations, then turn that reasoning into production code and durable architecture. * Principal-Level Judgment: You sequence work pragmatically, separate reversible from irreversible decisions, and design systems that can evolve without constant rewrites. * Product Impact: You connect architecture to OneSuite outcomes: trustworthy marketing insights, faster analyst workflows, reliable agent behavior, and reusable platform capabilities. * Leadership & Collaboration: You raise engineering quality through clear standards, strong reviews, mentoring, written architecture, and crisp communication across product, data, SRE, and engineering teams. ## Description We are seeking a Principal Backend Engineer (Individual Contributor) to set the technical direction for OneSuite data products and their integration into the broader platform. This is a hands-on principal role: you will write production-grade Python, design cross-repo architecture, lead high-risk technical decisions, and raise the standards other engineers use to build reliable systems. You will help build the backend and data foundation behind AI-assisted analyst workflows, including agents built with the Claude Agent SDK. That means turning complex marketing data into dependable services, tools, and agent experiences that can answer questions, surface issues, and support campaign decisions securely across clients. Architecture & Contracts * Own backend and data architecture across marketing data pipelines, connector systems, platform APIs, agent/tool runtimes, product CLIs, and downstream product surfaces. * Define durable domain boundaries and data contracts between warehouse outputs, operational sync targets, platform APIs, agent tools, and user-facing workflows. * Turn ambiguous platform needs into incremental technical plans that balance reliability, security, tenant isolation, developer experience, and long-term maintainability. Data & Connector Systems * Lead the evolution of Databricks medallion pipelines, including ingestion, normalization, and curated outputs for analytics and product use cases. * Guide data quality, schema enforcement, lineage, table documentation, and lakehouse-to-operational-store sync for consumer-facing data. * Harden ingestion from external marketing platforms and related APIs, including connector and reconciliation patterns. Platform Backend & Agent Integration * Architect Python backend services across AWS serverless and API paths, storage, authentication, and tenant-scoped access patterns. * Shape how platform capabilities are exposed through OneSuite, including streaming workflows, tool permissions, MCP integrations, and stable CLI/API/MCP contracts. * Create reusable patterns that can extend across current and future marketing intelligence domains. Engineering Quality, Reliability & Leadership * Write high-quality production code in Python and SQL, and stay close enough to implementation details to unblock difficult design and debugging problems. * Set standards for code review, testing, CI/CD, local development, deployment safety, observability, and operational readiness. * Design for real failure modes across external marketing APIs, third-party ingestion tools, data platforms, AWS services, tenant configuration, and agent/tool execution paths. * Mentor senior engineers through architecture review, pairing, practical standards, and clear written guidance rather than formal people management., * Own principal-level architecture for marketing data products and their integration into a global AI and analytics platform. * Work hands-on across Databricks, AWS serverless systems, agent runtimes, data contracts, and high-impact product surfaces. * Shape patterns that let OneSuite scale across current and future marketing intelligence domains. * Stay close to the code while influencing architecture, engineering standards, and technical direction across multiple teams and repositories. ## Related Videos - [Insights from building the Canva Developers Platform to empower 185 million designers](https://www.wearedevelopers.com/videos/942-insights-from-building-the-canva-developers-platform-to-empower-185-million-designers) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [Why make use of an integration platform in today's software developments and infrastructure?](https://www.wearedevelopers.com/videos/758-why-make-use-of-an-integration-platform-in-today-s-software-developments-and-infrastructure) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What’s the Difference Between Frontend and Backend Development?](https://www.wearedevelopers.com/magazine/240-what-s-the-difference-between-frontend-and-backend-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [The 7 Most Popular Backend Frameworks for Developers](https://www.wearedevelopers.com/magazine/403-the-7-most-popular-backend-frameworks-for-developers)