Key Responsibilities and Required Skills for Weather Engineer

🎯 Role Definition

A Weather Engineer (also titled Meteorological Engineer, Forecast Engineer, or NWP Engineer) designs, builds, and maintains the software, data pipelines, and scientific models that convert raw atmospheric, oceanic, and remote-sensing observations into actionable forecasts and decision-support products. This cross-disciplinary role blends atmospheric science, numerical weather prediction (NWP), data engineering, software development, and operational system reliability to deliver real-time forecast services, research-to-operations model improvements, and scalable cloud/HPC deployments.

Key search keywords: Weather Engineer, Meteorologist, Atmospheric Scientist, Numerical Weather Prediction, nowcasting, data assimilation, radar/satellite processing, model verification, high-performance computing, cloud forecasting.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Meteorologist / Operational Meteorologist transitioning to engineer-focused responsibilities
Data Engineer / Software Engineer with specialization in time-series and geospatial data
Research Scientist or Postdoctoral Researcher in atmospheric sciences or applied mathematics

Advancement To:

Senior Weather Engineer / Principal Meteorological Engineer
Lead NWP Engineer or Forecast Systems Architect
Head of Forecasting Engineering, Product Lead for Climate & Weather Services

Lateral Moves:

Data Scientist (specializing in spatiotemporal ML for weather)
Product Manager for Weather & Environmental Platforms

Core Responsibilities

Primary Functions

Design, implement, and maintain end-to-end data ingestion pipelines that collect, validate, normalize, and archive heterogeneous meteorological observations (radar, satellite, radiosonde, surface stations, IoT weather stations, aircraft reports) and model outputs for real-time and batch forecasting systems.
Develop, optimize, and deploy numerical weather prediction (NWP) model components and parameterizations (physics, boundary-layer, microphysics) into operational pipelines, ensuring model stability and performance across HPC and cloud clusters.
Implement and tune data assimilation systems (3D-Var, 4D-Var, EnKF, hybrid methods) to optimally merge observations with background model states, performing diagnostics to evaluate assimilation increments and observation impact.
Build and maintain automated model configuration, compilation, and deployment workflows using CI/CD tools to push research code to operational environments with reproducible builds and version control.
Architect scalable real-time and near-real-time forecasting platforms using Kubernetes, Docker, or HPC job schedulers to orchestrate ensemble runs, nowcasts, and multi-model pipelines.
Engineer verification and validation frameworks to compute skill scores (RMSE, CRPS, Brier, ROC, bias, spread-skill) and produce continuous model performance reports used to prioritize scientific improvements.
Design and implement ensemble generation, perturbation schemes, and post-processing systems (bias correction, MOS/ML-based calibration) to quantify forecast uncertainty and produce probabilistic products.
Implement robust quality control (QC) and sensor calibration routines for incoming observational data, including automated flagging, gap filling, and statistical outlier detection to ensure data integrity.
Create and maintain high-throughput ingestion and processing of remote sensing products (satellite radiances, radar reflectivity, lightning data), including radiative transfer transforms, geolocation, and gridding for assimilation and visualization.
Integrate third-party data sources and APIs (NOAA, ECMWF, MeteoFrance, WMO products) and manage licensing/ingest automation, metadata mapping, and standards compliance (e.g., CF-netCDF, BUFR).
Collaborate with data scientists and ML engineers to prototype, train, and productionize machine-learning nowcasting models for short-term precipitation, convective initiation, visibility, and other high-impact hazards.
Implement streaming and low-latency processing paths (Kafka, Redis Streams, edge compute) to support nowcasting and decision-support applications that require sub-minute updates.
Optimize numerical kernels and I/O performance (parallel I/O, MPI/OpenMP, vectorization) to reduce model wall-clock time on supercomputers and cloud instances while balancing accuracy and resource cost.
Lead integration of forecasting services into customer-facing APIs, dashboards, and alerting systems; define data contracts, SLAs, and monitoring to ensure reliable delivery to users and partners.
Perform operational run-day support, incident response, and root-cause analysis for model failures, data feed interruptions, and degraded forecast performance; implement automated failover and rollback mechanisms.
Translate scientific requirements into engineering specifications and partner with product managers to prioritize features, clarify acceptance criteria, and track ROI of model improvements.
Prepare technical documentation, model change logs, runbooks, and user guides for internal teams and external stakeholders to support reproducibility and knowledge transfer.
Conduct sensitivity experiments, feature-impact studies, and A/B testing to quantify the effect of code or data changes on forecast skill and operational metrics.
Collaborate with instrumentation engineers to specify, deploy, and maintain observation networks (surface stations, radars, lidars, disdrometers), including site selection, telemetry, and calibration plans.
Ensure compliance with relevant data governance, privacy, and security standards; implement authentication, authorization, and encryption for sensitive forecast and observational data.
Mentor junior engineers and scientists, run code reviews, and help establish engineering best practices for testing, monitoring, and scientific reproducibility.
Provide decision-support briefings and technical input to stakeholders during severe weather events; help tailor forecast products to customer-specific use cases (aviation, renewable energy, logistics, agriculture).
Manage resource allocation for model runs and cloud spend, leveraging cost-optimization strategies such as instance scheduling, spot/preemptible instances, and model downscaling when appropriate.
Stay current with advances in atmospheric science, remote sensing, ML-for-weather, and cloud/HPC technologies; evaluate, prototype, and recommend new tools or models for operational adoption.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Strong background in atmospheric science / meteorology with hands-on experience in numerical weather prediction (NWP), model configuration, and model output analysis.
Proven experience with data assimilation methods (3D-Var, 4D-Var, EnKF, hybrid) and operational assimilation systems.
Proficiency in scientific programming: Python (xarray, numpy, pandas), Fortran, and/or C++ for model code modification and performance tuning.
Experience with high-performance computing (HPC) environments: MPI, OpenMP, Slurm, LSF; knowledge of vectorization and memory optimization.
Cloud platform expertise (AWS, GCP, Azure) for provisioning compute/storage, container registries, serverless ingestion, and cost management.
Containerization and orchestration skills: Docker, Kubernetes, Helm, and related tooling for reproducible deployments.
Familiarity with remote sensing data processing: satellite radiances, radar reflectivity, lightning, nowcasting techniques, and radiative transfer models.
Expertise in observability: building monitoring/alerting (Prometheus, Grafana), logging, automated testing, and end-to-end data lineage.
Experience building ML-based post-processing and nowcasting models using frameworks such as TensorFlow or PyTorch, and feature engineering for spatiotemporal data.
Strong SQL and NoSQL skills for metadata, timeseries, and geospatial stores (PostGIS, InfluxDB, Cassandra).
Ability to write and maintain RESTful APIs and streaming pipelines (Kafka, MQTT) for delivering forecast products and telemetry.
Knowledge of geospatial formats and standards: netCDF/CF, GRIB, BUFR, GeoTIFF, WMS/WFS, GDAL, PROJ.
Proven track record in automated model verification, statistical evaluation, and forecast calibration techniques (MOS, EMOS, quantile mapping).
Experience with software engineering best practices: unit/integration testing, code reviews, CI/CD pipelines, and reproducible research workflows.
Familiarity with cybersecurity practices for data-in-transit and data-at-rest protection relevant to meteorological data services.

Soft Skills

Strong communication skills: explain complex meteorological models and engineering trade-offs to non-technical stakeholders and business leaders.
Cross-functional collaboration: work effectively with scientists, product managers, operations, and customers to deliver production-grade forecasting solutions.
Problem-solving: rapid troubleshooting during high-impact weather events and the ability to prioritize fixes under time pressure.
Project management: scope work, estimate tasks, manage deliverables, and track dependencies in agile environments.
Mentoring and team leadership: train junior staff, provide constructive feedback, and promote engineering and scientific rigor.
Curiosity and continuous learning: keep up with the latest research in atmospheric science, ML, and cloud/HPC technologies.
Customer focus: translate user needs and operational requirements into robust, usable forecast services.
Attention to detail: ensure data and model integrity through rigorous QC and verification processes.
Adaptability and resilience: working in a 24/7 operational forecasting culture with on-call rotations and incident responses.
Ethical judgment: handle sensitive environmental and user data responsibly and transparently.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Meteorology, Atmospheric Science, Computer Science, Applied Mathematics, or related STEM field.

Preferred Education:

Master's or PhD in Meteorology, Atmospheric Physics, Computational Science, or a closely related discipline, especially for roles involving model development or research-to-operations.

Relevant Fields of Study:

Atmospheric Science / Meteorology
Computer Science / Software Engineering
Applied Mathematics / Numerical Methods
Data Science / Machine Learning
Remote Sensing / Geospatial Science

Experience Requirements

Typical Experience Range: 3–8+ years combining operational forecasting, NWP model development, and software engineering; junior roles may start at 1–3 years with strong academic background.

Preferred:

5+ years in operational/weather-driven product environments or research groups with demonstrable operational deployments.
Prior work on production NWP systems, ensemble forecasting, or real-time nowcasting systems.
Experience deploying or operating forecasting stacks in cloud or HPC environments and managing mission-critical data pipelines.