2 months ago
Gurgaon, IndiaStaff+
Responsibilities
- Develop and monitor high-performance applications that deploy machine learning frameworks and advanced analytical techniques at scale.
- Design, develop, and maintain data and software pipelines and ETL processes for internal and external stakeholders.
- Explore large-scale data and develop architecture supporting model-building and analytical initiatives.
- Lead development of proprietary data engineering assets, machine learning algorithms, and analytical tools.
- Advocate data engineering best practices, code hygiene, code reviews, test-driven development, and CI/CD integration.
- Create documentation and operational runbooks for stakeholders and operational excellence.
- Work with Oliver Wyman consulting teams, partners, principals, and clients to understand challenges and deliver solutions.
- Contribute to proposals showcasing data engineering and analytics capabilities.
- Mentor junior engineers on data engineering concepts, platforms, and quality assurance processes.
- Travel to client locations internationally when required and keep current with emerging data engineering techniques.
Requirements
- Bachelor’s or master’s degree in Computer Science, Informatics, Data Science, or a related computational or quantitative discipline.
- Experience designing and deploying large-scale data solutions and cloud-native data applications.
- Mandatory fluency in Python; R and SAS are desired.
- Strong SQL skills and experience with relational databases such as MySQL, PostgreSQL, or Oracle.
- Familiarity with PySpark, Databricks, Snowflake, DBT, and modern storage and computational frameworks.
- Basic hands-on experience productizing data pipelines, building CI/CD pipelines, and orchestrating code with tools such as Airflow and DevOps tools.
- Hands-on knowledge of test-driven development and CI/CD pipeline integration.
- Strong mentoring, communication, collaboration, prioritization, and problem-solving skills.
- Willingness to travel internationally and respect for confidentiality and data security.
- Preferred qualifications include side projects or open-source contributions, awareness of Scikit-Learn or TensorFlow, Docker and Kubernetes experience, backend framework experience with FastAPI, Flask, or Django, and NoSQL experience with MongoDB or Cassandra.
- Hiring spans Data Engineer through Lead Data Engineer levels for experience ranging from 0–8 years.
Benefits
- Competitive total rewards package with comprehensive health and welfare benefits and employee assistance programs.
- Performance-based incentives may be available in addition to base salary.
- Hybrid work arrangement with remote flexibility and at least three days per week in the local office or onsite with clients.
- International client travel may be required.
Tech Stack
Apache AirflowApache CassandraAWSAzureDatabricksdbtDjangoDockerFastAPIFlaskGoogle Cloud PlatformKubernetesMongoDBMySQLPostgreSQLPythonRSassscikit-learnSnowflakeSQLTensorFlow
Categories
Data Engineering
About Marsh
Marsh is a global insurance broker and risk advisor that helps businesses, public entities, and individuals assess, transfer, and manage risk through insurance placement, captives, and advisory services. It sells brokerage, risk consulting, and claims management across industries, including cyber and property, and operates in more than 130 countries. Marsh is a business of Marsh McLennan and is headquartered in New York.