Data Analytics Lead Engineer

Citi

Confirmed live 2 days ago High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Irving, TX
Salary: $125,760–$188,640 / yr
Posted: 9 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $179k

This role $157k

$114k most similar roles pay here $234k

This role pays less than 69% of similar roles. Most pay $147,200–$211,200 — the shaded band above. At the midpoint, this role pays about $157k versus about $179k 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 256 open roles on FindRole.

Listed pay typically runs $140,080–$210,120 across 238 roles with salary data.

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At a glance

TL;DR · Data Analytics Lead Engineer

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As a Data Analytics Lead Engineer, you will join the Lending analytics engineering team to design, build, and operate scalable data pipelines and cloud-based architectures for Mortgage and Personal Loans. You will be responsible for developing production-grade systems, managing end-to-end data ingestion and transformation, and implementing complex schemas across data lakes and warehouses. The role requires hands-on experience with Hadoop, Apache Spark, PySpark, Databricks, Delta Lake, Hive, Impala, and Iceberg, alongside cloud platforms like Snowflake or Cloudera. You will utilize Python, Scala, or Shell Scripting for automation and orchestration via Autosys or Apache Airflow. Additionally, you will leverage SQL for large-scale data processing, integrate AI/ML services, and use tools like Tableau or Cognos to support reporting while ensuring high data quality across the lending product portfolio.

Skills

SQL Python Scala Apache Spark PySpark Databricks Snowflake Hadoop Delta Lake Hive Impala Iceberg Apache Airflow Autosys Tableau Cognos NoSQL MongoDB Cassandra AWS Azure GCP

What you'll do

Build, deploy, and manage end-to-end data pipelines for large-scale lending datasets across Mortgage and Personal Loans.
Design and implement scalable data architectures on cloud platforms including data lakes and warehouses.
Architect and implement various data schemas to meet specific performance and business requirements.
Write and optimize complex SQL queries using distributed and parallel processing to improve pipeline efficiency.
Monitor, diagnose, and resolve operational issues and data quality problems to ensure timely delivery.
Apply generative AI tools to accelerate engineering tasks like code generation and query optimization.
Translate business requirements into robust technical solutions in collaboration with analysts and governance teams.

What we're looking for

A Bachelor's degree or equivalent university qualification is required; a Master's degree is preferred.
6+ years of experience building and managing data pipelines, warehouses, and lake solutions using Hadoop, Spark, Databricks, and others.
Experience with cloud data platforms such as Snowflake or Cloudera to build and automate ETL workflows.
Fluency in Python, Scala, or Shell Scripting for data engineering and automation tasks.
Ability to design and query relational and non-relational data stores using various modeling principles.
Hands-on experience with workflow scheduling tools like Autosys or Apache Airflow.
Proficiency in DevOps practices including version control, build tools, unit testing, and monitoring.
Experience with data visualization platforms such as Tableau or Cognos.