This role pays more than
51%
of similar roles. Most pay
$142,300–$177,900
— the shaded band above.
At the midpoint, this role pays about
$157k
versus about
$160k
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
380 open roles
on FindRole.
Listed pay typically runs
$125,760–$188,640
across 354 roles with salary data.
As a senior data engineer at our technology team in Irving, Texas, you will collaborate closely with project managers and business stakeholders to design robust technical solutions for complex business requirements. Your day-to-day responsibilities include developing scalable data pipelines using PySpark, optimizing Spark jobs for performance, and ensuring data integrity through rigorous quality checks. You will also mentor mid-level developers and analysts, providing guidance on best practices in coding and testing. With a strong background in Big Data ecosystems like Cloudera and Apache Airflow, you will lead the resolution of high-impact problems by employing advanced analytical skills to uncover root causes and implement sustainable solutions. Additionally, you will contribute to data architecture design, ETL/ELT development, and performance optimization for PySpark jobs, ensuring seamless integration with various business needs including analytics and machine learning workflows.
Design and develop robust data pipelines using PySpark to support business needs.
Optimize Spark jobs for performance and efficiency in data processing tasks.
Implement data quality checks and ensure integrity across all data pipelines.
Mentor junior developers and provide technical guidance on coding standards.
Collaborate with stakeholders to understand data requirements and deliver solutions.
What we're looking for
6-10 years of experience in systems analysis and programming with a track record of successful project implementation.
Extensive hands-on experience with PySpark, advanced Python programming, and Big Data ecosystems like Cloudera or Data Bricks.
Proficient in designing complex workflows using scheduling tools such as Apache Airflow and distributed query engines like Starburst (Trino/Presto).
Strong expertise in SQL and both relational and non-relational databases, including experience with distributed caches like Apache GemFire.
Experience with real-time data streaming and processing using PySpark Structured Streaming and knowledge of machine learning concepts integrated with PySpark.
Familiarity with data visualization tools such as Tableau or Power BI and contributions to open-source data projects.