Senior Data Engineer

MSCI Inc; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

MSCI Inc. seeks a Senior Data Engineer to join the Data Operations team. You will ingest climate risk data, build pipelines, and coordinate with APIs and web services while ensuring QA/QC across geospatial datasets.

The role emphasizes SQL/Python development, GIS analytics, and scalable data solutions on local and cloud platforms, with a focus on reliability and reproducibility.

Qualifications

• Bachelor's degree in Data or Climate Science or related field.
• 5+ years of professional experience.
• Experience designing and using databases, e.g., PostgreSQL.
• Proficiency with SQL queries; Python also required.
• GIS knowledge and open-source geospatial tools.
• Strong probability and statistics for spatial data.
• Experience building data pipelines on local and cloud systems.
• Source control proficiency (Git).
• Science-based, reliable, accurate, reproducible work approach.
• Experience in GIS/geospatial statistics.
• Nice to have: DBT and Spark experience.

Responsibilities

• Provide technical support in processing geospatial observations and modeling data.
• Develop processes for large hazard data to improve risk assessment.
• Plan, execute workflows on local and cloud environments; use GDAL, PostgreSQL, Python, Spark.
• Analyze raster and vector data at scale to improve model accuracy.
• Perform statistical analysis to validate hazard predictions and uncertainties.
• Design QA checks on climate data and derived statistics.
• Assist in resolving customer support issues with quality control explanations.

Skills

SQL

Python

Geospatial analysis

Data pipelines

Cloud environments

PostgreSQL

Git

Statistical analysis

DS/QA concepts

Education

Bachelor's degree in data or climate science

Master's degree preferred

Tools

GDAL

PostgreSQL

Spark

DBT

Git

Job description

Team & Role Overview

We are looking for a Senior Data Engineer to join our team. The successful candidate will be someone who deeply cares about the environment, loves information technology, and appreciates the importance of data for the success of the First Street mission. They will assist in the ingestion of climate risk and ancillary data from First Street modelers and data partners, develop data pipelines, query geospatial databases, calculate applicable statistics, implement Quality Assurance and Quality Control processes, utilize geographical imagery, and ensure that databases and pipelines coordinate and synchronize with APIs, and web services. This individual’s expertise and leadership will enable all members of the Data Operations team to be successful in their roles.

• Provide technical support in the processing, analysis, and interpretation of geospatial observations and modeling data.

• Develop and implement processes for processing large volumes of hazard prediction data to improve risk assessment quality and accuracy.

• Plan, execute and workflows on local and cloud-based environments, using technologies such as GDAL, PostgreSQL, Python, Spark

• Analyze raster and vector data at scale to improve model accuracy, identify quality control issues, and develop suggested remedies for identified issues.

• Perform statistical analysis to validate hazard model predictions and assess model uncertainties.

• Design and implement quality assurance checks on the climate risk data and derived statistics

• Assist in resolution of customer support issues through quality control checks and explanation of the models and risk statistics

• Bachelor's Degree in Data or Climate Science, or a related field

• 5+ years of professional experience

• Data operations: Experience with the design and use of databases, such as PostgreSQL

• Programming: Proficiency with SQL queries to efficiently and reproducibly analyze complex datasets preferred. Additional languages like Python also required.

• GIS knowledge: Experience with writing software to efficiently process and analyze with geospatial data using open source tools

• Strong understanding of probability and statistics as applied to spatial data

• Expertise using scripted languages to build data pipelines on both local and cloud-based systems

• Proficiency with source control platforms such as Git

• A science-based approach with a high degree of concern for reliability, accuracy and reproducibility

• Experience in GIS and/or geospatial statistical analysis

• Nice to have: experience in DBT and Spark

What will make you stand out:

• Previous experience in the physical sciences

• Masters Degree Preferred