Senior Data Engineer

EPAM Systems; Brasil; Presencial; BRL 180 000 - 270 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Healthcare benefits

Global career opportunities

Training and certifications

Paid time off

LinkedIn Learning access

Resumo da oferta

EPAM Systems in Brazil is seeking a Senior Data Engineer to head design, development, and upkeep of data and ML pipelines on the Domino Data Lab platform. This role focuses on data engineering and MLOps, building reliable pipelines and managing model lifecycle workflows to keep data and compute infrastructure running securely and efficiently.

You will work with data scientists and engineers, promote best practices, automate testing and deployment, and monitor pipeline health to pre-empt

Qualificações

• 3+ years of relevant data engineering experience.
• Deep experience with Domino Data Lab platform and end-to-end pipelines.
• Expert Python for data engineering and pipeline development.
• Strong SQL skills across relational and warehouse systems.
• Experience with Kubernetes, Docker, and cloud services.
• CI/CD pipelines to automate tests and deployments.
• Fluent English (B2 or higher).

Responsabilidades

• Build and sustain dependable data pipelines for analytics and ML workloads.
• Manage full data workflow lifecycle: ingestion, transformation, delivery.
• Oversee compute infrastructure for secure, efficient platform operations.
• Diagnose and resolve pipeline performance and data quality issues.
• Collaborate with data scientists and engineers for tooling needs.
• Promote best practices for platform usage and data workflows.
• Automate testing and deployment to improve reliability.
• Monitor pipeline health and pre-empt bottlenecks or failures.
• Document designs, workflows, and configurations for knowledge sharing.

Conhecimentos

Python

SQL

Kubernetes

CI/CD

ETL/ELT pipelines

Data Pipelines

English Proficiency

Ferramentas

Git

Airflow

Jenkins

GitHub Actions

Descrição da oferta de emprego

We are looking for a Senior Data Engineer to head the design, development, and upkeep of data and ML pipelines on the Domino Data Lab platform. This position centers on the data engineering and MLOps aspects of Domino, constructing dependable data pipelines, overseeing model lifecycle workflows, and keeping the platform's data and compute infrastructure running efficiently and securely. This is not a front-end or application development role.

Responsibilities

• Build and sustain dependable data pipelines that power analytical and machine learning workloads
• Handle the full lifecycle of data workflows, spanning ingestion, transformation, and delivery
• Manage compute infrastructure to keep platform operations efficient, secure, and dependable
• Diagnose and resolve issues that affect pipeline performance and data quality
• Work alongside data scientists and other engineers to meet their infrastructure and tooling requirements
• Define and champion best practices around platform usage, pipeline design, and data workflow structure
• Introduce automation into testing and deployment processes to boost reliability and cut down manual work
• Keep watch over pipeline health and get ahead of bottlenecks or failures before they escalates
• Help drive continuous improvement of internal tools and processes that support data operations
• Record technical designs, workflows, and configurations to enable knowledge sharing throughout the team

Requirements

• At least 3 years of relevant experience
• Deep hands-on experience with the Domino Data Lab platform, including Data Sources and Connectors, Datasets, Environments, Projects, Jobs, and Flows, with the ability to architect and troubleshoot end-to-end data pipelines and promote best practices for platform usage
• Expert-level command of Python as the main language for data engineering and pipeline development
• Strong SQL skills for extracting, transforming, and optimizing data across relational and warehouse systems
• Comfortable using R and Bash across the wider data science toolchain and for scripting automation
• Proven experience designing, constructing, and maintaining ETL/ELT pipelines, including data ingestion, transformation, validation, and orchestration
• Practical experience with Kubernetes and managed offerings such as EKS, AKS, or GKE, with the ability to deploy, debug, and fine-tune cluster workloads running data and ML jobs
• Experience creating, optimizing, and troubleshooting container images for data and ML workloads using Docker
• Experience establishing and maintaining CI/CD pipelines using tools such as Jenkins, GitLab CI, GitHub Actions, or Azure DevOps to automate testing and deployment of data pipelines and ML workflows
• Working familiarity with at least one major cloud provider, such as AWS, Azure, or GCP, with the ability to evaluate data architecture, cost, and security trade-offs
• Excellent English proficiency (B2 level or higher)

Nice to have

• Experience with Domino Nexus or hybrid/multi-cloud compute orchestration
• Experience delivering ML workflows spanning training, deployment, monitoring, and retraining, with an understanding of reproducibility and versioning
• Practical experience with GenAI, LLMs, or agentic frameworks, including retrieval-augmented generation (RAG), from a data pipeline perspective
• Familiarity with orchestration tools such as MLflow, Kubeflow, Airflow, or Domino Flows
• Knowledge of model governance, compliance automation, or audit logging frameworks
• Experience working within pharma, BFSI, or public sector environments

We offer

• International projects with top brands
• Work with global teams of highly skilled, diverse peers
• Healthcare benefits
• Employee financial programs
• Paid time off and sick leave
• Upskilling, reskilling and certification courses
• Unlimited access to the LinkedIn Learning library and 22,000+ courses
• Global career opportunities
• Volunteer and community involvement opportunities
• EPAM Employee Groups
• Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn

EPAM is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, disability, protected veteran status, or any other characteristic protected by applicable law.