We are seeking highly experienced Data engineer to join our team.
Experience
- 5-8 years of experience in Data Engineering, Data Warehousing, and Cloud Data Platform development.
Data Engineering
- Strong hands-on experience with Snowflake, PySpark, Python, and SQL.
- Expertise in designing and developing scalable ETL/ELT pipelines for large-volume datasets.
- Experience with data ingestion, transformation, data quality, reconciliation, and performance optimization.
- Strong understanding of dimensional modeling, data warehousing concepts, and metadata-driven frameworks.
Cloud & Big Data
- Hands-on experience with AWS services such as S3, Glue, Lambda, EMR, Athena, Redshift, ECS/EKS, and IAM.
- Experience building cloud-native data solutions and modern data platforms.
- Strong understanding of distributed processing and big data architectures.
Snowflake
- Expertise in Snowflake architecture, SnowSQL, Streams, Tasks, Dynamic Tables, Snowpark, Data Sharing, and Performance Tuning.
- Experience implementing scalable data marts, semantic layers, and enterprise reporting solutions.
- Knowledge of Snowflake security, governance, and cost optimization best practices.
AI & Emerging Technologies
- Exposure to Generative AI, Agentic AI, LLMs, Vector Databases, and AI-powered analytics solutions.
- Experience leveraging AI services for data discovery, data quality, metadata management, or business insights.
- Understanding of RAG architectures, embeddings, and AI-assisted data engineering workflows.
DevOps & Automation
- Experience with Git, Jenkins, GitHub Actions, Terraform, and Infrastructure as Code.
- Familiarity with CI/CD pipelines and automated deployment of data workloads.
- Experience with workflow orchestration tools such as Airflow or similar platforms.
Data Integration
- Experience integrating data from relational databases, APIs, event streams, and enterprise applications.
- Exposure to CDC, real-time processing, Kafka, and event-driven architectures.
- Strong understanding of data lineage, governance, retention, and regulatory compliance requirements.
Soft Skills
- Strong analytical and problem-solving skills.
- Ability to work independently in a fast-paced Agile environment.
- Excellent communication and stakeholder management skills.
Preferred
- Experience with Oracle, Hadoop modernization, Ab Initio migration, or enterprise data platform transformation initiatives.
- Exposure to Data Mesh, Data Fabric, and modern analytics architectures.
- Financial Services or Regulated Industry experience.
- Experience building AI-ready data platforms and supporting advanced analytics use cases.
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Job Family Group: Technology------------------------------------------------------
Job Family:Applications Development------------------------------------------------------
Time Type:Full time------------------------------------------------------
Most Relevant Skills Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.
If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi.
View Citi’s EEO Policy Statement and the Know Your Rights poster.
Skills Required
- 5-8 years of experience in data engineering, data warehousing, and cloud data platform development
- Hands-on experience with Snowflake, PySpark, Python, and SQL
- Experience designing and developing scalable ETL/ELT pipelines for large-volume datasets
- Experience with data ingestion, transformation, data quality, reconciliation, and performance optimization
- Understanding of dimensional modeling, data warehousing concepts, and metadata-driven frameworks
- Hands-on experience with AWS services including S3, Glue, Lambda, EMR, Athena, Redshift, ECS/EKS, and IAM
- Experience building cloud-native data solutions and modern data platforms
- Understanding of distributed processing and big data architectures
- Expertise in Snowflake architecture, SnowSQL, Streams, Tasks, Dynamic Tables, Snowpark, Data Sharing, and performance tuning
- Experience implementing scalable data marts, semantic layers, and enterprise reporting solutions
- Knowledge of Snowflake security, governance, and cost optimization best practices
- Exposure to Generative AI, Agentic AI, LLMs, Vector Databases, and AI-powered analytics solutions
- Experience leveraging AI services for data discovery, data quality, metadata management, or business insights
- Understanding of RAG architectures, embeddings, and AI-assisted data engineering workflows
- Experience with Git, Jenkins, GitHub Actions, Terraform, and Infrastructure as Code
- Familiarity with CI/CD pipelines and automated deployment of data workloads
- Experience with workflow orchestration tools such as Airflow or similar platforms
- Experience integrating relational databases, APIs, event streams, and enterprise applications
- Exposure to CDC, real-time processing, Kafka, and event-driven architectures
- Understanding of data lineage, governance, retention, and regulatory compliance requirements
- Strong analytical and problem-solving skills
- Ability to work independently in a fast-paced Agile environment
- Excellent communication and stakeholder management skills
- Experience with Oracle, Hadoop modernization, Ab Initio migration, or enterprise data platform transformation initiatives
- Exposure to Data Mesh, Data Fabric, and modern analytics architectures
- Financial Services or regulated industry experience
- Experience building AI-ready data platforms and supporting advanced analytics use cases
Citi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Citi and has not been reviewed or approved by Citi.
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Healthcare Strength — Benefits coverage is positioned as comprehensive, including health, dental, and vision insurance plus on-site clinics, prescription drug support, and disability coverage. Family-building support such as fertility assistance is described as a notable differentiator within the overall package.
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Retirement Support — Retirement benefits are framed as strong, highlighted by a 401(k) with matching and additional plan options like a Roth 401(k). Financial support is reinforced through discounts and broader financial guidance resources tied to the benefits ecosystem.
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Wellbeing & Lifestyle Benefits — Wellbeing support extends beyond insurance through programs like an Employee Assistance Program, counseling/legal resources, and gym or wellness reimbursement. These offerings increase the perceived total rewards value even when cash compensation sentiment varies by role.
Citi Insights
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