Data Analytics Lead Engineer

About the role

Citi is looking for a Data Analytics Lead Engineer to design, build, and operate scalable data pipelines and cloud‑based data architectures within the 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.

Qualifications

• 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.

Skills

• 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.

Benefits

• Medical, dental & vision coverage.
• 401(k) retirement plan.
• Life, accident, and disability insurance.
• Wellness programs.
• Paid time off packages: planned time off (vacation), unplanned time off (sick leave), and paid holidays.
• Discretionary and formulaic incentive and retention awards for eligible employees.
• Continuous learning and technical development support, including backing for cloud and platform certifications relevant to your work.
• Collaborative team of data engineers, analysts, and business partners where hands‑on technical contribution is valued and visible.
• Access to large‑scale, complex data environments and modern cloud infrastructure where engineering decisions have direct business impact.

Pay

Salary range: $125,760.00 – $188,640.00 per year.