Senior Data Engineer - C13 - IRVING

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About Citi:

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 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.

WHAT WE ARE LOOKING FOR:

We are seeking a highly motivated and experienced Apps Dev Tech Lead Analyst - Senior Data Engineer to join our Data Engineering/Consumer Data Platform team. In this role, you will be a key player in designing, building, and maintaining robust and scalable data pipelines and solutions that leverage cutting-edge Big Data technologies, including AI and NLP.

The ideal candidate is a high-impact individual with a passion for data and analytics. You are a problem-solver at heart, with a proven ability to collaborate across teams and drive innovative solutions. You are passionate about using data to drive customer engagement and growth. This role demands a deep understanding of data engineering principles, experience with distributed systems, and a passion for applying AI and NLP techniques to solve real-world business challenges. You will be responsible for the volume, quality, timeliness, and delivery of end results, and may also be involved in planning, budgeting, and policy formulation within your area of expertise. This role does few of management position, with responsibilities including performance evaluations, hiring and respective duties.

 Do you have what it takes to join our team? We are looking for candidates who share our passion for tackling complex data challenges head-on and are driven to build the next generation of data and analytics platforms.

Responsibilities:

• Design, develop, and maintain scalable and efficient data pipelines using Big Data technologies (e.g., Hadoop, Spark, Kafka, Hive, Parquet, Avro) to ingest, process, and transform large volumes of structured and unstructured data.

• Implement and optimize ETL/ELT processes for data ingestion, cleansing, transformation, and loading into data warehouses, data lakes, and other data stores.

• Integrates subject matter and industry expertise within a defined area.

• Applies in-depth understanding of how data engineering and analytics collectively integrate within the sub-function as well as coordinates and contributes to the objectives of the entire function.

• Build and maintain data pipelines outcomes as data federations layers for lambda and Data Mesh architecture using tools like Starburst with strategy for adopting AI and NLP techniques-based use cases to drive efficiency and reduce data copies (e.g., machine learning, deep learning, natural language processing) to extract insights, automate processes, and enhance decision-making.

• Develop and deploy microservices-based architectures to support data-intensive applications and ensure scalability, resilience, and maintainability.

• Collaborate with data scientists, business analysts, and other stakeholders to understand data requirements, design appropriate solutions, and deliver value-added insights.

• Mentor and guide junior team members, fostering a culture of knowledge sharing and technical excellence.

• Ensure data quality, integrity, and security throughout the data lifecycle.

• Contribute to the continuous improvement of data engineering processes, standards, and best practices.

• Effectively communicate technical concepts and solutions to both technical and non-technical audiences.

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