Data Engineering at Citi
Roles, Comp & Culture
An L5 senior data engineer at Citi sits around $235K total comp from 47 verified salary datapoints. The primary Data Engineering tech consists of Hadoop, Spark and Docker, according to current job listings. Citi pays data engineers above other Finance companies. Reviews put them at 3.6 on Glassdoor, a little below the middle of the pack. Employee sentiment at Citi reads neutral and employee happiness has held flat over the past year. Layoff risk scores low for the next 30 days. 26 data engineering roles are open right now.
Citi data engineer compensation
Each level's figure is the median of individual Citi offers at that level, so it reflects a typical outcome rather than an average pulled up by a few large packages. Total comp counts base salary plus equity and bonus annualized over the vest, and the range shown is the middle half of offers, with the top and bottom quarters trimmed off.
Citi employee sentiment, tracked weekly
Employee happiness for data engineers over the past year, so you can see which direction it is moving, not just where it sits today.
Citi data engineering tech stack
The languages, storage, and processing tools Citi data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
SQL
IcebergCiti data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
Data integration delivery: Design, build, and operate robust batch and near-real-time integration pipelines for CRM data domains (e.g., customers, products, orders, invoices, service interactions).
Big Data Infrastructure: Develop and manage large-scale data processing systems using frameworks like Apache Spark, Hadoop, and Kafka.
Design and maintain blueprint of the information architecture, data integrations and controls aligned to the renewed business strategy
Design, develop, and maintain high-performance, resilient, and scalable ETL processes using the Ab Initio suite of products (GDE, Co>Operating System, EME) to transform upstream data into the required format for Oracle Financials SaaS.
Develop, maintain, and optimize highly efficient and resilient data ingestion, processing, and transformation pipelines using advanced Python and PySpark techniques for large-scale datasets.
Architect for Real-Time: Serve as the go-to expert for the data platform, ensuring every design adheres to our overall architecture blueprint.
Build and maintain big data pipelines using technologies such as Apache Hadoop, Apache Kafka, Databricks and other cloud based big data tools.
Monitor and control all phases of development process and analysis, design, construction, testing, and implementation as well as provide user and operational support on applications to business users
Architect & Design: Design, architect, and oversee the development of robust, scalable, and reliable data infrastructure, including data lakes, data warehouses, and real-time streaming platforms on the cloud.
Architect, design, and deliver scalable, Python‑based applications supporting credit risk analytics, workflows, and reporting.
Practice for the Citi loop
Round by round, the problems our model predicts for this company's interview. Rehearse the shapes their panels keep returning to.
Preparing for the Citi loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Compare Citi with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Citi interview difficulty
- 01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
- 02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
- 03
5 problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition