Data Engineer

Who is ERM?

ERM is a leading global sustainability consulting firm, committed for nearly 50 years to helping organizations navigate complex environmental, social, and governance (ESG) challenges. We bring together a diverse and inclusive community of experts across regions and disciplines, providing a truly multicultural environment that fosters collaboration, professional growth, and meaningful global exposure. As a people-first organization, ERM values well-being, career development, and the power of collective expertise to drive sustainable impact for our clients—and the planet.

Introducing our new Global Delivery Centre (GDC)

Our Global Delivery Centre (GDC) in India is a unified platform designed to deliver high-value services and solutions to ERM's global clientele. By centralizing key business and consulting functions, we streamline operations, optimize service delivery, and enable our teams to focus on what matters most—advising clients on sustainability challenges with agility and innovation. Through the GDC, you will collaborate with international teams, leverage emerging technologies, and further enhance ERM's commitment to excellence—amplifying our shared mission to make a lasting, positive impact.

ERM is seeking a Data Engineer to join our Digital Products team. This is an exciting opportunity for an early- to mid-career data professional to support cutting-edge climate science by engineering high-performance data pipelines and workflows in Azure and Microsoft Fabric. The successful candidate will help operationalize global climate model data and other scientific datasets by applying algorithms developed by ERM's climate scientists and consultants, supporting large-scale climate risk assessments across sectors including energy, finance, infrastructure, and natural resources.

You'll work alongside a team of experts in climate science, software development, and environmental consulting. You'll also have access to a global network of technical specialists and opportunities to grow your skills within a purpose-driven organization.

RESPONSIBILITIES:

• Develop, test, and maintain scalable data pipelines in Azure and Microsoft Fabric to process climate and environmental data.
• Support the application of scientific algorithms to global climate model outputs and other multidimensional datasets (e.g., NetCDF, Zarr).
• Optimize data pipelines for performance, scalability, and reliability, including benchmarking storage read/write speeds and tuning Spark-based workflows.
• Implement logging, monitoring, and metadata management systems for traceability and auditability.
• Evaluate and configure Azure storage (Blob, Data Lake, etc.) for climate-scale workloads; ensure performance alignment with data access needs.
• Build and manage distributed processing pipelines to support large-scale data transformations using PySpark and parallel computing strategies.
• Monitor and optimize I/O performance across workflows and storage tiers.
• Collaborate with climate scientists and software engineers to translate prototype code into scalable, production-ready processes.
• Document data engineering workflows and support internal data governance efforts.

REQUIREMENTS:

• Bachelor's degree in Computer Science, Data Science, Environmental/Climate Science, Oceanography, or a related field.
• 2+ years of experience in data engineering, including pipeline development and workflow optimization (cloud-based preferred).
• Hands-on experience with Microsoft Azure data services and/or Microsoft Fabric.
• Strong programming skills in Python; experience with Spark (PySpark or SparkSQL) in distributed environments.
• Demonstrated experience working with large, multidimensional datasets (Zarr, NetCDF, HDF5).
• Familiarity with cloud storage performance tuning and benchmarking techniques.
• Experience implementing logging, monitoring, and metadata practices in data environments.
• Strong communication skills, with ability to collaborate across technical and non-technical teams.
• Preferred: Azure Data Engineering certification, or demonstrable equivalent experience.