Senior Data Engineer

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 escalate
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.

Work arrangement
Yes