This role pays less than
63%
of similar roles. Most pay
$133,425–$211,200
— the shaded band above.
At the midpoint, this role pays about
$157k
versus about
$172k
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
281 open roles
on FindRole.
Listed pay typically runs
$125,760–$188,640
across 263 roles with salary data.
Data Engineering Manager, Vice President serves as a technical leader within the Data Analytics team, overseeing the design and delivery of scalable data solutions to power regulatory reporting, business decision-making, and analytical capabilities. The role involves building high-availability data pipelines, architectural models, and distributed systems while mentoring engineers and establishing engineering standards in an agile environment. Key responsibilities include managing the data supply chain, ensuring quality through documentation, and translating complex technical concepts for stakeholders. Required skills include proficiency in SQL, Python, Apache Spark, and tools like Hadoop, Hive, Databricks, or Snowflake. Candidates must also possess experience with Parquet, Avro, Iceberg, CI/CD, Autosys, and shell scripting. The role addresses critical data management challenges within the financial sector, specifically focusing on large-scale data infrastructure for regulatory and business use cases.
Design and build scalable, high-availability data pipelines and analytical models for regulatory and business reporting.
Architect data lake solutions, batch/streaming pipelines, and distributed systems to support diverse use cases.
Identify and resolve risks within the data supply chain before they impact downstream consumers.
Establish engineering standards, best practices, and technical documentation to improve team quality.
Translate complex technical concepts into clear, actionable language for non-technical business stakeholders.
Mentor and coach junior and mid-level engineers to foster a culture of technical excellence.
Lead technical discussions and participate in hiring decisions to build team capabilities.
What we're looking for
Must have 6 or more years of experience delivering data-intensive solutions in agile environments.
Must possess hands-on expertise building production-grade ETL pipelines using Apache Spark, Hadoop, Hive, Databricks, or Snowflake.
Must have a solid grounding in data warehousing concepts, file/table formats (Parquet, Avro, Iceberg), and data catalog technologies.
Must be proficient in SQL and Python for data engineering delivery with familiarity in CI/CD, Autosys, and shell scripting.
Must possess practical knowledge of data management and governance disciplines.
Must have a bachelor's degree or equivalent experience.
Knowledge of containerization (Docker, Kubernetes), access controls, and Generative AI tools (preferred); a master's degree is an advantage (preferred).
Snowflake
BigQuery
dbt
Airflow
Data Warehousing
ETL
ELT
Data Lakehouse
Parquet
Iceberg
Data Dog
Anomalo
Data Architecture
Advanced Analytics
Data Science
Data Quality