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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