Data Analytics Lead Engineer

Citigroup; Irving (TX); Hybrid; USD 130,000 - 180,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Citi is seeking a Data Analytics Lead Engineer to design, build, and operate scalable data pipelines and cloud-based data architectures for Lending (Mortgage and Personal Loans). The role is hands-on and involves production-grade data systems across big data platforms and data lakes, with AI/ML integration as a capability.

You will develop, monitor, and optimize pipelines, ensure data quality, and collaborate with analysts and governance teams to translate business needs into robust technical

Qualifications

• 6+ years of hands-on experience building and managing data pipelines, data warehouses, and data lake solutions.
• Experience with cloud data platforms and scalable ETL/ELT processes.
• Fluency in Python, Scala, or Shell scripting for data engineering and automation tasks.

Responsibilities

• Build end-to-end data pipelines for large-scale lending datasets with high reliability and performance.
• Design scalable cloud data architectures across data lakes, warehouses, and streaming environments.
• Architect data schemas to meet performance and business requirements; optimize SQL queries for large datasets.

Skills

Data pipelines

Data warehouses

Data lakes

Apache Spark

PySpark

Databricks

SQL

Python

Airflow

Hadoop

Education

Bachelor's degree or equivalent

Master's degree preferred

Tools

Databricks

Snowflake

Airflow

Autosys

Tableau

Cognos

Job description

Citi is looking for a Data Analytics Lead Engineer to design, build, and operate scalable data pipelines and cloud-based data architectures within our Lending business, spanning Mortgage and Personal Loans. This is a hands‑on data engineering role where you will develop and maintain production‑grade data systems - working across big data platforms, data lakes, and cloud infrastructure - that directly power lending analytics at scale. You will also bring an understanding of AI and ML integration as an additional capability applied within a strong data engineering foundation.

Responsibilities

• Build, deploy, and manage end-to-end data pipelines that ingest, transform, and deliver large-scale lending datasets across Mortgage and Personal Loans with high reliability and performance.
• Design and implement scalable data architectures on cloud platforms, selecting the right tools and approaches across data lakes, data warehouses, and streaming environments.
• Architect and implement data schemas - choosing from relational, dimensional, normalized, or partitioned models - to meet performance, scalability, and business requirements.
• Write and optimize complex SQL queries against large-scale datasets, applying sound decisions around distributed and parallel processing to improve pipeline efficiency.
• Monitor, diagnose, and resolve operational and data quality issues across pipelines to ensure accuracy, completeness, and timely delivery of data.
• Apply generative AI tools to accelerate core engineering tasks such as code generation, query optimization, and data summarization where appropriate.
• Contribute to data engineering standards and collaborate with Business Analysts, Data Engineers, and Data Governance teams to translate business requirements into robust technical solutions.

Required Qualifications & Skills

• 6+ years of hands‑on experience building and managing data pipelines, data warehouses, and data lake solutions using technologies such as Hadoop, Apache Spark, PySpark, Databricks, Delta Lake, Hive, Impala, and Iceberg.
• Practical experience with cloud data platforms including Snowflake, Cloudera, used to build and automate ETL and data ingestion workflows.
• Fluency in one or more scripting languages - Python, Scala, or Shell Scripting - applied actively to data engineering, pipeline development, and automation tasks.
• Strong ability to design and query relational and non‑relational data stores, with a clear understanding of schema design trade‑offs and data modelling principles.
• Hands‑on experience with workflow scheduling tools such as Autosys or Apache Airflow to manage and orchestrate data pipeline execution.
• Confident use of DevOps practices including version control, build tools, unit testing, monitoring, and change management to support reliable and repeatable delivery.
• Experience with data visualization platforms such as Tableau, Cognos to support data presentation and reporting needs.
• A Bachelor's degree or equivalent university qualification; a Master's degree is preferred.

Beneficial Skills & Qualifications

• Exposure to cloud‑based AI and ML services such as Amazon SageMaker, Azure Machine Learning, or Google AI Platform, used to integrate predictive models within data pipelines.
• Familiarity with NoSQL database technologies such as HBase, MongoDB, Couchbase, Cassandra, or Neo4j.
• Databricks certification or cloud platform certification in AWS, Azure, or GCP.
• A proactive approach to troubleshooting - able to independently investigate root causes and resolve pipeline or data issues with thoroughness and pace.

What We Offer

At Citi, you will work as a practicing data engineer at the center of one of the world's largest financial institutions, solving complex, real‑world data challenges across lending products that serve millions of customers globally. We offer the technical scale and team environment to do meaningful engineering work, alongside the flexibility and investment to support your continued growth.

• Hybrid working model with 3 days in the office and 2 days working remotely, providing flexibility alongside team collaboration.
• Access to large‑scale, complex data environments and modern cloud infrastructure where your engineering decisions have direct business impact.
• Continuous learning and technical development support, including backing for cloud and platform certifications relevant to your work.
• A collaborative team of data engineers, analysts, and business partners where hands‑on technical contribution is valued and visible.
• Competitive compensation and a comprehensive benefits package designed to support your financial and personal wellbeing.