Senior Data Engineer

MSCI

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
New York, NY
Salary
$125,000–$163,000 / yr
Posted
2 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $168k
This role $144k
$115k most similar roles pay here $216k

This role pays less than 68% of similar roles. Most pay $136,275–$199,100 — the shaded band above. At the midpoint, this role pays about $144k versus about $168k for comparable roles.

Based on 240 similar postings.

Employer

About MSCI

MSCI is a US-based provider of investment decision support tools, including equity indexes, portfolio risk and performance analytics, and ESG research used by institutional investors worldwide.

MSCI currently has 13 open roles on FindRole.

Listed pay typically runs $125,000–$187,000 across 13 roles with salary data.

Most-posted roles

View all roles at MSCI

At a glance

TL;DR · Senior Data Engineer

The Senior Data Engineer will join the Data Operations team to support the ingestion of climate risk and ancillary data while developing robust data pipelines. This role involves processing large volumes of hazard prediction data, performing statistical analysis to validate model predictions, and implementing quality assurance processes for climate risk data and derived statistics. The candidate will work with geospatial databases, query raster and vector data at scale, and ensure systems synchronize with APIs and web services. Key technical requirements include proficiency in Python, SQL, Spark, and DBT, alongside experience with PostgreSQL, GDAL, and Git. The role requires a strong understanding of probability and statistics as applied to spatial data. This position focuses on the technical challenge of improving risk assessment accuracy through the analysis of geospatial observations and modeling data.

What does a Data Engineer earn?

Median $162000 from 195 postings across 59 companies.

See salary data

What you'll do

  • Ingest climate risk and ancillary data from modelers and partners into production systems.
  • Develop and implement automated data pipelines using Python, Spark, and SQL in local and cloud environments.
  • Query and manage geospatial databases to process large volumes of hazard prediction data.
  • Analyze raster and vector data at scale to improve model accuracy and identify quality issues.
  • Perform statistical analysis to validate hazard model predictions and assess uncertainty.
  • Design and implement quality assurance checks on climate risk data and derived statistics.
  • Resolve customer support issues by performing quality control checks and explaining complex risk models.

What we're looking for

  • Bachelor's Degree in Data or Climate Science, or a related field.
  • 5+ years of professional experience.
  • Proficiency with Python and SQL for analyzing complex datasets (preferred).
  • Experience designing and using databases such as PostgreSQL.
  • Expertise using scripted languages to build data pipelines on local and cloud-based systems.
  • Experience writing software to process and analyze geospatial data using open source tools like GDAL.
  • Strong understanding of probability and statistics applied to spatial data.
  • Proficiency with Git and experience in DBT or Spark (preferred); Master's Degree (preferred).

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