Data Analytics Leader

7 days ago


Jersey City, New Jersey, United States Citigroup Inc Full time

Citigroup Inc. is a global bank with approximately 200 million customer accounts across more than 160 countries and jurisdictions. Our core activities include safeguarding assets, lending money, making payments, and accessing capital markets on behalf of our clients.

As a Senior Data Engineer at Citigroup Inc., you will play a strategic role in developing Big Data solutions that drive business growth and profitability. This is an exciting opportunity to work with a talented team of data engineers, domain experts, product managers, analysts, and data scientists to design and develop scalable data pipelines in Hadoop for regulatory requirements.

We are looking for a highly skilled professional with experience in data warehousing, ETL, and Big Data technologies such as Hadoop, Spark, and Kafka. The ideal candidate will have hands-on experience with coding languages such as PySpark, Snowflake, and Google Cloud/AWS cloud platforms.

The successful candidate will be responsible for:

  • Designing and developing Big Data solutions that meet business requirements
  • Migrating legacy workloads to EAP platform for regulatory reporting
  • Defining needs around maintainability, testability, performance, security, quality, and usability for data platform
  • Implementing consistent patterns, reusable components, and coding standards for data engineering processes
  • Tuning Big Data applications on Hadoop and non-Hadoop platforms for optimal performance

Our total compensation package for this role includes a salary range of $142,320 - $213,480 per annum, plus additional benefits such as medical, dental, and vision coverage, 401(k), life, accident, and disability insurance, and paid time off packages.

Key Responsibilities:

  • Develop Big Data pipelines in Hadoop for regulatory requirements
  • Migrate legacy workloads to EAP platform for regulatory reporting
  • Define needs around data platform maintainability, testability, performance, security, quality, and usability
  • Implement consistent patterns, reusable components, and coding standards for data engineering processes
  • Tune Big Data applications on Hadoop and non-Hadoop platforms for optimal performance


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