AVP Data Solutions Engineer — Hybrid Cloud Pipelines

Citibank (Switzerland) AG; Irving (TX); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Citi is seeking a Data Solutions Engineer to join a dynamic team building data pipelines and platforms for analytics, reporting, and data science. This hybrid role spans Irving, TX and Jacksonville, FL, offering hands-on development in scalable cloud-based data solutions.

You will work with Python, PySpark, Spark, Hadoop, and cloud services to support ML features, collaborate with data scientists, and help improve governance and data quality while mentoring peers.

Qualifications

• 5+ years building large-scale data pipelines in production.
• Strong Python and PySpark for data processing at scale.
• Experience with cloud data platforms (AWS or GCP).
• Knowledge of data warehousing concepts and distributed processing.
• Familiarity with Spark, Hadoop, Kafka, Hive and related tech.
• Ability to drive patterns and coding standards in data engineering.
• Experience with Docker and Kubernetes for containerized workflows.

Responsibilities

• Build and maintain scalable data pipelines supporting analytics and ML.
• Develop big data solutions using Spark, Hadoop, and cloud services.
• Collaborate with data scientists to enable advanced analytics.
• Contribute to data platform designs with architects and engineers.
• Apply data quality controls, monitoring, and governance standards.
• Mentor junior engineers and share best practices.

Skills

5+ years experience

Python

PySpark

Cloud platforms (AWS/GCP)

Data warehousing concepts

Spark/Hadoop

Kafka/Hive

Docker/Kubernetes

Communication

Tools

Docker

Kubernetes

Spark

Hadoop

Kafka

Hive

Databricks

Snowflake

Job description

Citi is seeking a Data Solutions Engineer to join a dynamic team building data pipelines and platforms for analytics, reporting, and data science. This hybrid role spans Irving, TX and Jacksonville, FL, offering hands-on development in scalable cloud-based data solutions.

You will work with Python, PySpark, Spark, Hadoop, and cloud services to support ML features, collaborate with data scientists, and help improve governance and data quality while mentoring peers.