Senior Backend Engineer, Data
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Job Description
Senior Backend & Data Engineer building Python services and Databricks pipelines for Publicis Groupe’s unified marketing intelligence platform. Powering analytics, automation, and agentic workflows across marketing channels.
Responsibilities:
- Build and maintain Databricks and PySpark data pipelines using medallion-style Bronze/Silver/Gold architecture
- Implement data quality checks, schema enforcement, and lineage tracking across pipeline stages
- Build and optimize ETL/ELT workflows for marketing performance data at scale
- Collaborate with analytics and data science teams on clean, well-modeled, accessible data assets
- Build backend services and APIs in Python using FastAPI, Flask, or equivalent frameworks
- Work within established microservices, domain-boundary, caching, and queuing patterns
- Build service-to-service communication using REST, gRPC, and event-driven patterns
- Integrate with external marketing platforms, cloud services, and internal microservices
- Implement secure authentication, authorization, rate limiting, and multi-tenant access controls
- Write production-ready code, participate in code reviews, and mentor other engineers through technical influence
- Build and maintain CI/CD pipelines and infrastructure-as-code environments
- Implement tracing, structured logging, metrics, and alerting
- Optimize services for latency, concurrency, throughput, and cost efficiency
- Contribute to automated testing, load testing, and resiliency practices
- Partner with SRE on SLIs/SLOs
- Collaborate with product, data, and SRE teams on shared backend designs across campaign activation domains
Requirements:
- At least 5 years of experience in backend engineering, data engineering, or distributed systems
- Strong, hands-on expertise in Python, with experience in FastAPI, Flask, or equivalent modern backend frameworks
- Strong, hands-on proficiency with Databricks and PySpark, including practical experience with medallion-style (Bronze/Silver/Gold) data architecture — or a strong willingness and aptitude to pick it up quickly
- Solid understanding of microservices, concurrency, async programming, and event-driven architectures
- Proficiency with SQL and NoSQL databases, including PostgreSQL, DynamoDB, Redis, MongoDB, or similar
- Familiarity with lakehouse platforms
- Hands-on experience with Docker, Kubernetes, and AWS
- Experience with infrastructure-as-code tooling such as CloudFormation
- Strong CI/CD experience, such as GitHub Actions or Argo
- Good working knowledge of observability practices: logs, metrics, traces, and performance analysis
- Proven ability to write clean, testable, well-structured production code
- Fluent in English, both verbal and written
- Familiarity with AI/LLM systems and agentic workflow integration
- Experience with streaming or queuing systems such as Kafka, Pub/Sub, or SQS
- Experience with Delta Lake, Unity Catalog, or other lakehouse governance and optimization tools
- Prior experience with API gateway architectures, caching layers, or service meshes
- Familiarity with marketing, advertising, or platform APIs and similar ecosystems
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