Data Engineer

The Applications Development Programmer Analyst

Citi is seeking a Applications Development Programmer Analyst 2 (C10) to join its Technology – Data Engineering function, focused on the PySpark / Spark-based Data Platform. In this role, you will design, develop, and maintain scalable data pipelines and analytical solutions, supporting large-scale data operations across enterprise systems. You will work within a full software development lifecycle (SDLC) environment spanning Development (NAM), UAT, and Production.

activities.

Core Technical Duties

Design, develop, test, and maintain PySpark and Apache Spark data pipelines for batch and streaming workloads.

Manage and monitor Spark Streaming Jobs (running Sunday evening through Saturday afternoon) performing Eligibility and Data Quality (DQ) checks, publishing data to Kafka sender topics, and processing exception/XMEV requests.

Manage and monitor Spark Batch Jobs (running hourly or every 2 hours) that store audit, inbound, and outbound data into Hive tables (e.g., gfolyreg_managed.SWXPTT_audit).

Respond to ITRS Alerts and system monitoring events (e.g., job failures, kills, or jobs not triggered within 30 minutes of scheduled time).

Work with large-scale data sources across Oracle, Hadoop, Impala, and SAS Server.

Configure and manage JDBC connections from SAS, Python, and R to dedicated Sandbox/data platform environments.

Collaborate on schema creation during code deployment; raise CMP tickets for permission grants and country-specific schema access.

Support the retirement of legacy applications and champion adoption of modern data engineering technologies.

Technical Stack

Tool / Technology - Role in Platform

Python / PySpark - Primary language for data pipeline development

Apache Spark - Distributed compute engine for large-scale data workloads

Spark Streaming - Real-time data processing and Kafka integration

Spark Batch - Scheduled ETL jobs writing to Hive tables

SAS Server - Main analytical tool with Oracle/Hadoop/Impala access

Hadoop / HDFS / Hive - Distributed storage, batch processing, and table management

Impala - Fast SQL querying on Hadoop data

Kafka - Event streaming and messaging platform

Oracle / JDBC - Relational database connectivity

Autosys - Job scheduling, alerting, and monitoring

Required Skills & Qualifications

Must Have:

• Proficiency in Python and PySpark for distributed data engineering.
• Experience with Apache Spark (Streaming and Batch) in production environments.
• Strong understanding of SQL and ability to work with relational (Oracle) and columnar (Impala, Hive) databases.
• Familiarity with Kafka or event-driven architectures.
• Experience with job scheduling and monitoring tools (e.g., Autosys, ITRS).
• Knowledge of data governance, access management, and information security standards.
• Strong problem-solving skills for diagnosing and resolving complex data pipeline issues.

Good to Have:

• Experience with SAS or equivalent enterprise analytical platforms.
• Familiarity with SDLC practices across Development, UAT, and Production environments. ( GitHub / Light speed )

Qualifications:

• 2-5 years of relevant experience
• Experience in programming/debugging used in business applications
• Working knowledge of industry practice and standards
• Comprehensive knowledge of specific business area for application development
• Working knowledge of program languages
• Consistently demonstrates clear and concise written and verbal communication

Education:

• Bachelor’s degree/University degree on information technology or Engineer or equivalent experience

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

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Job Family:

Applications Development

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Time Type:

Full time

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Most Relevant Skills

Please see the requirements listed above.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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