Data Analytics Engineer

Overview of the Role:

Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments, and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management.

As a bank with a brain and a soul, Citi creates economic value that is systemically responsible and in our clients’ best interests. As a financial institution that touches every region of the world and every sector that shapes your daily life, our Enterprise Operations & Technology teams are charged with a mission that rivals any large tech company. Our technology solutions are the foundations of everything we do from keeping the bank safe, managing global resources, and providing the technical tools our workers need to be successful to designing our digital architecture and ensuring our platforms provide a first-class customer experience. We reimagine client and partner experiences to deliver excellence through secure, reliable, and efficient services.

Our commitment to diversity includes a workforce that represents the clients we serve from all walks of life, backgrounds, and origins. We foster an environment where the best people want to work. We value and demand respect for others, promote individuals based on merit, and ensure opportunities for personal development are widely available to all. Ideal candidates are innovators with well-rounded backgrounds who bring their authentic selves to work and complement our culture of delivering results with pride. If you are a problem solver who seeks passion in your work, come join us. We’ll enable growth and progress together.

The Data Analytics Engineer is a hands-on technical role within the Data Analytics team, responsible for designing, building, and optimizing scalable data pipelines, data transformation frameworks, and analytics solutions across distributed cloud and enterprise big data platforms. This position combines core data engineering expertise in Python, PySpark, Snowflake, Databricks, and Hadoop with a modern engineering mindset that leverages AI Coding Agents and developer tools to accelerate delivery and maintain high code quality. The role requires close collaboration with business analysts, data scientists, and functional stakeholders to translate complex business challenges into resilient, high-performance data products. Candidates should bring 2+ years of progressive experience in data engineering or big data analytics, along with strong analytical thinking, problem-solving, and cross-functional communication skills. This opportunity offers exposure to large-scale legacy-to-cloud data migration efforts alongside the adoption of next-generation, AI-augmented engineering practices.

Responsibilities:

• •Design, implement, and maintain robust, end-to-end data pipelines for batch and real-time processing using Python, PySpark, Databricks, and Snowflake.
• •Manage, migrate, and optimize legacy datasets and workloads across enterprise Hadoop ecosystems to modern cloud data platforms.
• •Optimize pipeline performance, query execution times, resource allocation, and storage costs across distributed environments.
• •Partner with business analysts, data scientists, and functional stakeholders to understand requirements and formulate analytical approaches. Deliver actionable insights that address complex business challenges.
• •Apply structured root-cause analysis and analytical problem-solving techniques to diagnose data anomalies, pipeline bottlenecks, and complex logic issues.
• •Create automated data validation, reconciliation, and quality monitoring frameworks to ensure high data integrity and compliance.
• •Actively incorporate next-generation AI Coding Agents and developer assistants, such as Devin, GitHub Copilot, and Cursor, into daily development, code reviews, automated unit testing, and technical documentation workflows.
• •Drive engineering productivity by adopting best practices in automated refactoring, rapid prototyping, and AI-augmented debugging.
• •Stay current with emerging tools, data architecture, and AI-enabled data engineering methodologies.
• •Follow disciplined CI/CD release practices, Git-based version control, code quality gates, and automated test coverage.
• •Ensure all data pipelines and storage solutions comply with enterprise data governance, data lineage, information security, and regulatory standards.
• •Author comprehensive technical design documents, data dictionary definitions, and operational runbooks.

Qualifications:

• •2+ years of progressive experience in Data Engineering, Big Data Analytics, or a related technical role.
• •Bachelor's or Master's degree in Computer Science, Data Analytics, Information Technology, Statistics, Engineering, or a related quantitative field, or equivalent practical experience.
• •Advanced proficiency in Python, including Pandas, PySpark, and NumPy. Strong understanding of object-oriented programming, data structures, and modular design.
• •Strong hands-on experience developing large-scale distributed data transformations in PySpark and Databricks.
• •Proven expertise in Snowflake architecture and SnowSQL. Hands-on experience with data loading and unloading using Snowpipe, Tasks, and Streams, as well as clustering and query profiling.
• •Solid understanding of the Hadoop ecosystem, including HDFS, Hive, MapReduce, and YARN. Experience migrating and integrating Hadoop workloads with modern cloud architectures.
• •Expert-level SQL skills for complex querying, window functions, and dimensional and relational data modeling.
• •Practical knowledge and working experience using AI Coding Agents and assistants, such as Devin, GitHub Copilot, and Cursor, to boost engineering velocity and code reliability.
• •Proven ability to break down complex analytical problems, design scalable technical solutions, and communicate findings clearly to diverse stakeholders.
• •Familiarity with workflow orchestration tools, such as Apache Airflow, Prefect, or Databricks Workflows, is preferred.
• •Experience with CI/CD tools, such as Git, Jenkins, Tekton, or Harness, is preferred. Familiarity with containerization basics, such as Docker, is also preferred.
• •Exposure to financial services, banking domain data, or risk and compliance data platforms is preferred.
• •Familiarity with DataOps, test-driven development, and automated data quality frameworks, such as Great Expectations, is preferred.

Education:

Bachelor’s or Master’s degree in Computer Science, Data Analytics, Information Technology, Statistics, Engineering, or a related quantitative field (or equivalent practical experience).

Preferred / Nice-to-Have Skills:

Familiarity with workflow orchestration tools (e.g., Apache Airflow, Prefect, Databricks Workflows).

Experience with CI/CD tools (Git, Jenkins, Tekton, Harness) and containerization basics (Docker).

Exposure to financial services, banking domain data, or risk/compliance data platforms.

Familiarity with DataOps, test-driven development (TDD), and automated data quality frameworks (e.g., Great Expectations).

This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.

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Job Family Group:

Technology ------------------------------------------------------

Job Family:

Applications Development ------------------------------------------------------

Time Type:

Full time ------------------------------------------------------

Primary Location:

Irving Texas United States ------------------------------------------------------

Primary Location Full Time Salary Range:

• $84 580,00 - $118 020,00
• In addition to salary, Citi’s offerings may also include, for eligible employees, discretio