Senior ETL Developer - Assistant Vice President

Citi

Remote

Quick summary

Work type: Remote
Location: Remote
Salary: $96,960–$145,440 / yr
Posted: 3 days ago
Closes: Jul 16, 2026

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $168k

This role $121k

$84k most similar roles pay here $220k

This role pays less than 91% of similar roles. Most pay $142,300–$194,172 — the shaded band above. At the midpoint, this role pays about $121k versus about $168k for comparable roles.

Based on 240 similar postings.

Employer

About Citi

Citi is one of the world’s most trusted financial institutions, proudly serving millions of customers across the United States.

Citi currently has 365 open roles on FindRole.

Listed pay typically runs $125,760–$188,640 across 342 roles with salary data.

Most-posted roles

View all roles at Citi

At a glance

TL;DR · Senior ETL Developer - Assistant Vice President

Apply Now Log in to save

The Senior ETL Developer role involves designing and implementing distributed data processing jobs using Apache Spark and Python within the Hadoop ecosystem to handle large-scale datasets. This position requires a deep understanding of data engineering principles and hands-on experience with Spark and Hadoop frameworks such as Hive and Sqoop. Day-to-day responsibilities include developing efficient ETL pipelines for data ingestion, cleaning, aggregation, and transformation, optimizing Spark jobs for performance, and ensuring data accuracy and integrity across various sources. The ideal candidate will have 5-8 years of relevant software development experience and strong proficiency in Python and Apache Spark, with a focus on troubleshooting and debugging complex data processing tasks to support business stakeholders in driving data-driven decisions.

Skills

Python Apache_Spark Hadoop HDFS Spark Hive Sqoop ETL CI/CD Docker Kubernetes AWS Google_Cloud_Platform Azure PostgreSQL MySQL MongoDB Git Jenkins Prometheus Grafana

What you'll do

Design and implement Spark applications to process large datasets in HDFS.
Develop ETL pipelines using Python for data ingestion, cleaning, and transformation.
Optimize Spark jobs to reduce runtime and resource usage efficiently.
Integrate Spark applications with Hadoop frameworks like Hive and Sqoop.
Troubleshoot and debug Spark job failures by analyzing logs and UI metrics.

What we're looking for

5-8 years of experience in software development with a focus on data engineering.
Proficiency in Python and hands-on experience with Apache Spark and Hadoop ecosystems.
Expertise in designing and implementing ETL pipelines for large-scale data processing.
Deep understanding of data engineering principles, including performance optimization techniques.
Ability to collaborate effectively with data engineers, analysts, and business stakeholders.
Experience in troubleshooting and debugging complex distributed systems like Apache Spark jobs.

Save