The Applications Development Technology Lead Analyst is a senior level position responsible for establishing and implementing new or revised application systems and programs in coordination with the Technology team. The overall objective of this role is to lead applications systems analysis and programming activities.

Responsibilities:

• Partner with multiple management teams to ensure appropriate integration of functions to meet goals as well as identify and define necessary system enhancements to deploy new products and process improvements

• Resolve variety of high impact problems/projects through in-depth evaluation of complex business processes, system processes, and industry standards

• Provide expertise in area and advanced knowledge of applications programming and ensure application design adheres to the overall architecture blueprint

• Utilize advanced knowledge of system flow and develop standards for coding, testing, debugging, and implementation

• Develop comprehensive knowledge of how areas of business, such as architecture and infrastructure, integrate to accomplish business goals

• Provide in-depth analysis with interpretive thinking to define issues and develop innovative solutions

• Serve as advisor or coach to mid-level developers and analysts, allocating work as necessary

• Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.

• 7+ years of experience in data engineering, with a strong focus on Python and big data technologies.

• Proven expertise in designing and implementing large-scale data processing solutions using PySpark.

• Extensive experience with distributed computing frameworks like Apache Spark.

• Strong understanding of data warehousing concepts, dimensional modeling, and ETL/ELT principles.

• Proficiency in SQL and experience with various relational and NoSQL databases.

• Experience with cloud platforms (AWS, Azure, GCP) and their data services (e.g., S3, ADLS, Google Cloud Storage, Redshift, Snowflake, BigQuery, Databricks).

• Familiarity with workflow orchestration tools (e.g., Apache Airflow, Azure Data Factory, AWS Step Functions).

• Experience with version control systems (e.g., Git).

• Excellent problem-solving, analytical, and communication skills.

Preferred

• Experience with streaming data technologies (e.g., Kafka, Kinesis).

• Familiarity with containerization technologies (Docker, Kubernetes).

• Knowledge of data governance, data lineage, and metadata management tools.

• Experience with CI/CD pipelines for data solutions.

• Certifications in cloud data engineering (e.g., AWS Certified Data Analytics, Google Cloud Professional Data Engineer).

Technical Skills

Programming Languages

• Python: Advanced proficiency, including data manipulation libraries (Pandas, NumPy) and object-oriented programming.

• PySpark: Expert-level knowledge for data processing, transformations, and performance tuning on Spark.

• SQL: Advanced proficiency for complex queries, database design, and optimization.

Big Data Frameworks

• Apache Spark (PySpark)

Data Warehousing & Databases

• Data Modeling (Star Schema, Snowflake Schema)

• ETL/ELT Methodologies

• Relational Databases (e.g., PostgreSQL, MySQL, SQL Server, Oracle)

• Cloud Data Warehouses (e.g., Snowflake, Amazon Redshift, Google BigQuery)

• NoSQL Databases (e.g., MongoDB, Cassandra, DynamoDB)

Cloud Platforms (good to have)

• AWS: S3, EMR, Glue, Lambda, Redshift, Athena, Kinesis

• Azure: Data Lake Storage, Databricks, Synapse Analytics, Azure Data Factory, Event Hubs

• GCP: Cloud Storage, Dataproc, BigQuery, Dataflow, Pub/Sub

Orchestration & Automation

• Apache Airflow

• Azure Data Factory

• AWS Step Functions

Other Tools & Concepts

• Version Control (Git, GitHub, GitLab, Bitbucket)

• Containerization (Docker, Kubernetes)

• CI/CD Principles

• Data Governance & Security

• Performance Optimization & Tuning