C

Citi

Runs banking and payments for global clients

Data Analytics Lead Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeIrving, TX, USA

About the Job

Requirements

  • At least 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 and Cloudera to build and automate ETL and data ingestion workflows.
  • Fluency in one or more scripting languages—Python, Scala, or shell scripting—applied to data engineering, pipeline development, and automation tasks.
  • Ability to design and query relational and non-relational data stores, with an 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 and Cognos to support data presentation and reporting needs.
  • A bachelor's degree or equivalent university qualification.

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 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.

Desired Qualifications

  • A master's degree.
  • 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.

About the Company

Citi

Runs banking and payments for global clients

Citi runs banking, investment and payment services for companies, governments and individuals around the world. Its global network connects institutions with local banking systems, while its US consumer business sells credit cards and deposit accounts. Corporate clients use Citi to manage cash, finance trade and raise capital, and investors use its trading, custody and wealth services to hold and manage assets. The bank earns interest on loans and fees from payments, securities transactions, financial advice and account services. Citi traces its banking roots to 1812; the Citigroup holding company was formed in 1998, and its headquarters are in New York City.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1812

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