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全职 Research Associate (Data Engineering) 工作, 薪水, National University Of Singapore Central Region (Singapore) 公司招聘中 - Ricebowl

Research Associate (Data Engineering)

National University Of Singapore

Undisclosed

全职

Queenstown, Central Region (Singapore)

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工作地点

  • Queenstown Central Region (Singapore) Singapore

职位描述

岗位职责

Job Title: Research Associate (Data Engineering)
University-Level Unit: College of Design and Engineering
Faculty/Department-Level Unit: Civil and Environmental Engineering
Employee Category: Research Staff
Location_ONB: Kent Ridge Campus
Posting Start Date: 12/06/2026

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

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