Research Associate (Data Engineering)

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Associate-%28Data-Engineering%29/33302-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

We are seeking a skilled and motivated Research Associate to join our environmental informatics team. In this role, the candidate will build and maintain the data infrastructure that underpins our environmental monitoring and early-warning systems. The work will involve diverse, high-volume data streams — including rainfall records, temperature sensors, radar imagery, and computer vision outputs — to deliver a unified, query table, and secure data platform that drives research, operational decision-making, and stakeholder dashboards. 

Key Responsibilities
1.  Environmental Data Platform
•    Design, build, and maintain a unified database to ingest and store diverse environmental data streams: rain gauge records, gridded temperature data, rainfall radar (e.g. OPERA, NEXRAD), satellite imagery, and computer vision model outputs.
•    Define and enforce common data schemas and ontologies across heterogeneous source formats (NetCDF, HDF5, GeoTIFF, CSV, JSON, REST/API feeds).
•    Implement scalable ingestion pipelines supporting real-time streaming and batch historical loads.
•    Ensure data traceability with robust metadata, provenance tracking, and versioning.

2.  Data Processing & Quality Assurance
•    Develop and maintain automated pipelines for data cleaning, outlier detection, and quality flagging.
•    Implement missing-data imputation methods appropriate to environmental time-series and spatial fields (e.g. interpolation, climatological fill, ML-based gap-filling).
•    Apply noise-removal algorithms (e.g. signal filtering, radar clutter suppression, spike detection) across sensor and remote-sensing data types.
•    Document processing logic and maintain reproducible workflow configurations. 

3.  Visualisation & Dashboards
•    Design and develop interactive dashboards for operational and research users, displaying spatial maps, time-series plots, and aggregated statistics.
•    Integrate visualisation tools (e.g. Grafana, Superset, Plotly Dash, or custom web front-ends) with the data backend.
•    Collaborate with domain scientists to translate monitoring requirements into effective visual analytics.
•    Ensure dashboards remain performant and responsive under live data load. 

4.  Data Security & Governance
•    Implement and maintain role-based access control (RBAC) for all data assets.
•    Enforce data encryption at rest and in transit; manage secrets and credentials securely.
•    Support compliance with relevant data governance policies and institutional data-sharing agreements.
•    Maintain audit trails and access logs; respond to security reviews and risk assessments.

5.  Infrastructure & Operations
•    Manage cloud or on-premise database services (e.g. PostgreSQL/PostGIS, TimescaleDB, InfluxDB, or equivalent); tune for time-series and geospatial query performance.
•    Maintain CI/CD pipelines for data pipeline code; apply version control best practices.
•    Monitor pipeline health, set up alerting for failures, and respond to incidents.
•    Contribute to infrastructure-as-code practices (Docker, Kubernetes, Terraform or equivalent)

Job Requirements

•    Possess at least a Master’s degree in computer science, Data Engineering, Environmental Informatics, or a closely related field.
•    3–6 years of professional experience in data engineering, with demonstrable work on time-series or geospatial data.
•    Proficiency in Python (pandas, NumPy, xarray, or similar) and SQL; experience with at least one workflow orchestration tool (Airflow, Prefect, Luigi, etc.).
•    Hands-on experience with geospatial or scientific data formats: NetCDF, HDF5, GeoTIFF, GeoJSON, or similar.
•    Working knowledge of relational and time-series databases, with practical experience in data modelling.
•    Familiarity with cloud platforms (AWS, GCP, or Azure) and containerisation (Docker).
•    Solid understanding of data security principles: encryption, RBAC, secrets management. 
•    Open to fixed-term contract. 

Experience
•    Experience with environmental, meteorological, or hydrological datasets (radar QPE, NWP outputs, IoT sensor networks).
•    Familiarity with PostGIS or other spatially enabled database extensions.
•    Exposure to machine learning pipelines or MLOps practices for model output ingestion.
•    Experience building dashboards with Grafana, Apache Superset, Plotly Dash, or equivalent.
•    Contributions to open-source scientific data tooling (e.g. xarray, GDAL ecosystem).

Core Competencies
•    Preferably knowledge with Python, SQL, Bash
•    Time-series & geospatial DBs
•    Data pipeline orchestration
•    Cloud & containerisation
•    Dashboard & BI tools
•    Data security & RBAC