Data Engineer

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.
• Implement monitoring, logging, IAM, secrets management, and cost optimisation.
• Collaborate with AI, geospatial, platform, and product teams on deployment and scaling issues.

Selection Criteria

Educational Qualification

• B.E./B.Tech in Computer Science, IT, or a related discipline, or equivalent relevant experience.
• Relevant cloud or data engineering certifications are preferred.

Must Have

• 5+ years of experience in data engineering, including production-grade data pipelines.
• Strong proficiency in Python and SQL.
• Experience with ETL/ELT, data modelling, APIs, relational databases, and data lake or warehouse architectures.
• Hands-on experience with GCP or AWS data and infrastructure services.
• Experience with PostgreSQL/PostGIS or similar databases.
• Experience with orchestration tools such as Airflow or Prefect and processing frameworks such as Spark or Dask.
• Working knowledge of Linux, Docker, CI/CD, cloud networking, monitoring, and infrastructure operations.
• Strong understanding of data quality, security, performance, and cost optimisation.

Preferred

• Experience with geospatial data stacks and tools such as PostGIS, GeoPandas, Rasterio, Xarray, GeoServer, or TiTiler.
• Experience supporting AI/ML workloads, GPU environments, model serving, or MLOps platforms.
• Familiarity with vector databases, embedding pipelines, and RAG systems.
• Exposure to Kubernetes and infrastructure-as-code tools such as Terraform.
• Experience with government cloud, Digital Public Infrastructure, climate tech, or public-data systems.

Compensation

Competitive compensation – commensurate with the experience and matching the best of the standards adopted by the industry or other similar organisations for similar roles.

Application Process

CEEW is an equal-opportunity employer, and the selection process does not discriminate on the basis of age, gender, caste, ethnicity, religion, or sexuality. Female candidates are encouraged to apply.

Applications will be reviewed on a rolling basis. Interested applicants are advised to apply at the earliest possible time.

Only shortlisted candidates will be notified by us. We appreciate your interest.