Data Engineer (India)

Data Engineer (India)

06 Oct
|
Council On Energy Environment And Water
|
India

06 Oct

Council On Energy Environment And Water

India

Council on Energy, Environment and
Water (CEEW), New Delhi

DevOps
Engineer - Tech and AI

Terms of reference

At CEEW, we are deliberate about what we stand
for (and what we don’t stand for) as an employer. If the description below
resonates with you, we would love to hear from you

At CEEW, we build careers in public policy

· We offer strong visionary
leadership – with emphasis on research and impact at scale

· We actively promote leadership by
initiative

· We celebrate talent and ambition

· You will be surrounded by smart
people who will challenge you and help you grow

· You will learn faster than your
peers in other organisations

· Curiosity and irreverence, as well
as responsibility, come together at CEEW

· You will get above-market
remuneration

· We provide a secure space for all

· At CEEW, your life is your example
for others

Designation offered

Data Engineer

Focus area

Technology & AI

Location

New Delhi, India

Duration:

Full- Time

Job duties and accountabilities

Roles &
Responsibilities

Data Engineering




- Design and maintain batch and real-time data pipelines using Python, SQL, and suitable orchestration tools.
- Ingest and transform data from APIs, databases, satellite imagery, public datasets, and external sources.
- Build cloud-based data lakes, warehouses, and curated datasets on GCP or AWS.
- Develop data models and optimise storage, partitioning, indexing, and query performance.
- Build geospatial ETL workflows for formats such as NetCDF, Zarr, GeoTIFF/COG, and vector data.
- Prepare reliable datasets for analytics, ML training, inference, RAG, and application APIs.
- Implement data quality checks, lineage, documentation, monitoring, retries, and backfills.

Cloud and DevOps
- Provision and manage cloud infrastructure required for data, geospatial, and AI workloads.
- Build CI/CD pipelines for data pipelines, backend services, and ML workloads.
- Use Docker and services such as Kubernetes, Cloud Run, or equivalent deployment platforms

📌 Data Engineer (India)
🏢 Council On Energy Environment And Water
📍 India

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