Lead Data Engineer & DevOps: Cloud, DataOps & CI/CD

Procter & Gamble

Województwo mazowieckie

Hybrid

PLN 220,000 - 320,000

Full time

4 days ago

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Benefits offered by this job

Industry certifications program

Private health care

Stock options

Saving plans

Lunch subsidy

Flexible work arrangements

Job summary

Procter & Gamble's Beauty Care IT Hub in Warsaw is seeking a Lead Data Engineer & DevOps to guide the JBP, upskill technical resources, and define best practices across data platforms. You will lead architecture, data pipelines, and DataOps initiatives in a global context.

As a senior leader, you will mentor teams, own cloud strategy, and drive secure, scalable CI/CD with observability and FinOps discipline. Hybrid work in Warsaw supports collaboration and delivery excellence.

Qualifications

Proven senior experience in data engineering and/or management
Experience with Azure and/or Google Cloud Platform
Demonstrated knowledge of: SQL, Python, PySpark, coding standards
Client management and communication skills
Hands on experience writing clean, effective code

Responsibilities

Define Long-Term Platform Architecture: Partner with Data Asset Manager, Solution/Enterprise architects to establish a resilient, future-proof technical roadmap.
Lead Data Pipeline Engineering: Architect and optimize robust, high-throughput batch and streaming data pipelines leveraging the Google Cloud ecosystem (current solution). Assembling large, complex data sets which meet functional and non-functional business requirements.
Champion DataOps & Infrastructure as Code (IaC): Own the DevOps strategy for data platforms to provision and manage cloud infrastructure, ensuring environments are reproducible, version-controlled, and modular.
Automate End-to-End CI/CD: Build, maintain, and standardize automated CI/CD pipelines incorporating automated testing, data contract validation, and zero-downtime deployment practices.
Drive GCP FinOps & Observability: Implement SRE-style monitoring, logging, and alerting to maintain high availability while actively managing and optimizing GCP cloud spend and resource allocation.
Embed Security & Governance into Code: Implement enterprise security controls and data governance standards directly into deployment pipelines and infrastructure configurations.
Technical Leadership & Mentorship: Guide and upskill engineering teams across both modern data engineering techniques and DevOps principles, establishing best practices for code reviews, architectural documentation, and production readiness.