Lead Data Engineer & DevOps
Do uzgodnienia
Wymagania
- Python
- SQL
- PySpark
- Google Cloud Platform
- Databricks
Opis stanowiska
About the Company
Beauty Care is one of the company’s sectors, merging categories of Skin, Personal Care and Hair Care with billion dollar brands like Olay, Old Spice, SK-II, Pantene, Head & Shoulders and Herbal Essences. As Beauty Care IT Hub in Warsaw we provide IT services and solutions to Beauty Care business globally. The roles in Warsaw focus around Data & Analytics, DevOps and Architecture, which includes Software Engineering, Data & AI Engineering, Data Asset Management and Product Management.
Role Overview
We are looking for a Lead Data Engineer & DevOps to join our Beauty Care IT team in Warsaw to lead the JBP with central, upskill our technical resources, define best practices/processes, etc.
Responsibilities
As a Lead Data Engineer & DevOps you will be responsible for:
- 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. Acquiring, cleansing, transforming, validating and publishing data from a wide variety of sources.
- 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.
Beauty Care is one of the company’s sectors, merging categories of Skin, Personal Care and Hair Care with billion dollar brands like Olay, Old Spice, SK-II, Pantene, Head & Shoulders and Herbal Essences. As Beauty Care IT Hub in Warsaw we provide IT services and solutions to Beauty Care business globally. The roles in Warsaw focus around Data & Analytics, DevOps and Architecture, which includes Software Engineering, Data & AI Engineering, Data Asset Management and Product Management.
Role Overview
We are looking for a Lead Data Engineer & DevOps to join our Beauty Care IT team in Warsaw to lead the JBP with central, upskill our technical resources, define best practices/processes, etc.
Responsibilities
As a Lead Data Engineer & DevOps you will be responsible for:
- 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. Acquiring, cleansing, transforming, validating and publishing data from a wide variety of sources.
- 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.
🔍 Dekoder Ogłoszenia
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