Citi Group Data Engineer - Controls Technology 

India, Tamil Nadu, Chennai 

840885075

Data Pipeline Development:

• Design, build, and optimize ETL/ELT pipelines for structured and unstructured data.
• Develop real-time and batch data ingestion pipelines using distributed data processing frameworks.
• Ensure pipelines are highly performant, cost-efficient, and secure.

Apache Iceberg & Starburst Integration:

• Work extensively with Apache Iceberg for data lake storage optimization and schema evolution.
• Manage Iceberg Catalogs and ensure seamless integration with query engines.
• Configure and maintain Hive MetaStore (HMS) for Iceberg-backed tables and ensure proper metadata management.
• Utilize Starburst and Stargate to enable distributed SQL-based analytics and seamless data federation.
• Optimize performance tuning for large-scale querying and federated access to structured and semi-structured data.

Data Mesh Implementation:

• Implement Data Mesh principles by developing domain-specific data products that are discoverable, interoperable, and governed.
• Collaborate with data domain owners to enable self-service data access while ensuring consistency and quality.

Hybrid Cloud Data Integration:

• Develop and manage data storage, processing, and retrieval solutions across AWS and on-premise environments.
• Work with cloud-native tools such as AWS S3, RDS, Lambda, Glue, Redshift, and Athena to support scalable data architectures.
• Ensure hybrid cloud data flows are optimized, secure, and compliant with organizational standards.

Data Governance & Security:

• Implement data governance, lineage tracking, and metadata management solutions.
• Enforce security best practices for data encryption, role-based access control (RBAC), and compliance with policies such as GDPR and CCPA.

Performance Optimization & Monitoring:

• Monitor and optimize data workflows, performance tuning of queries, and resource utilization.
• Implement logging, alerting, and monitoring solutions using CloudWatch, Prometheus, or Grafana to ensure system health.

Collaboration & Documentation:

• Work closely with data architects, application teams, and business units to ensure seamless integration of data solutions.
• Maintain clear documentation of data models, transformations, and architecture for internal reference and governance.

Programming & Scripting:

• Strong proficiency in Python, SQL, and Shell scripting.
• Experience with Scala or Java is a plus.

Data Processing & Storage:

• Hands-on experience with Apache Spark, Kafka, Flink, or similar distributed processing frameworks.
• Strong knowledge of relational (PostgreSQL, MySQL, Oracle) and NoSQL databases (DynamoDB, MongoDB).
• Expertise in Apache Iceberg for managing large-scale data lakes, schema evolution, and ACID transactions.
• Experience working with Iceberg Catalogs, Hive MetaStore (HMS), and integrating Iceberg-backed tables with query engines.
• Familiarity with Starburst and Stargate for federated querying and cross-platform data access.

Cloud & Hybrid Architecture:

• Experience working with AWS data services (S3, Redshift, Glue, Athena, EMR, RDS).
• Understanding of hybrid data storage and integration between on-prem and cloud environments.

Infrastructure as Code (IaC) & DevOps:

• Experience with Terraform, AWS CloudFormation, or Kubernetes for provisioning infrastructure.
• CI/CD pipeline experience using GitHub Actions, Jenkins, or GitLab CI/CD.

Data Governance & Security:

• Familiarity with data cataloging, lineage tracking, and metadata management.
• Understanding of RBAC, IAM roles, encryption, and compliance frameworks (GDPR, SOC2, etc.).

Required Soft Skills:

• Problem-Solving & Analytical Thinking - Ability to troubleshoot complex data issues and optimize workflows.
• Collaboration & Communication - Comfortable working with cross-functional teams and articulating technical concepts to non-technical stakeholders.
• Ownership & Proactiveness - Self-driven, detail-oriented, and able to take ownership of tasks with minimal supervision.
• Continuous Learning - Eager to explore new technologies, improve skill sets, and stay ahead of industry trends.

Qualifications:

• 4-6 years of experience in data engineering, cloud infrastructure, or distributed data processing.
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, or a related field.
• Hands-on experience with data pipelines, cloud services, and large-scale data platforms.
• Strong foundation in SQL, Python, Apache Iceberg, Starburst, and cloud-based data solutions (AWS preferred), Apache Airflow orchestration

Time Type:

View the " " poster. View the .

View the .

View the