Job Description
Global Controllers Technology is looking for a hands-on data engineer who is passionate about solving business problems through innovation and engineering practices. In this role, you will leverage your deep technical knowledge to drive the creation of a scalable and high-quality data intensive platform leveraging some of the latest methodologies such as Data Virtualization and Data Domains. The ideal candidate should have a strong foundation in data engineering principles, understanding of different data pipelines and the ability to optimize and scale data processing and analytics solutions.
Qualifications:
Core Data Engineering Skills
- 5+ years overall experience in Big Data or Enterprise large-scale application development using scalable tools like Databricks, Scala, Java, and the Python ecosystem.
- Deep understanding of data modeling, data warehousing concepts, methodologies, and best practices.
- Strong experience with data processing (e.g., Spark, Starburst, Snowflake, Redshift), storage (e.g., Hadoop, MongoDB, Oracle), and ETL (e.g., Airflow, Ab Initio, Talend) solutions.
- Experience with data engineering techniques such as building data lakes and data warehouses, data mesh architectures, data pipelines, and ETL vs. ELT patterns.
- Strong expertise in Starburst/Trino with deep understanding of data federation and data virtualization architectures.
- Experience with real-time data processing and data virtualization technologies such as Starburst, Snowflake, and Flink (preferred).
- Experience with designing and developing distributed systems, handling structured and unstructured data to store, analyze, and report.
- Experience with developing and maintaining large and complex database systems using relational databases like Oracle, and other big data technologies like Hadoop, Spark, etc.
- Proficient in PL/SQL and Unix Shell scripts. Strong SQL, PL/SQL development, and database tuning experience.
- Experience in designing Online Transaction Processing (OLTP), Operational Data Store (ODS), and Data Warehouse applications.
- Experience with Data Quality and data lineage.
AI Agents, Orchestration & Applied AI
- Demonstrable experience with AI Agents, including their design, development, and integration into data engineering workflows and software systems.
- Practical knowledge of Model Context Protocol (MCP) servers, including:
- Setting up and configuring MCP servers to expose data tools, database connections, and APIs as contextual resources for LLM-powered agents.
- Designing MCP-compatible tool interfaces that allow agents to query, transform, and validate data autonomously.
- Understanding MCP's client-server architecture and its role in enabling secure, standardized communication between AI agents and enterprise data systems.
- Understanding of Retrieval-Augmented Generation (RAG) principles, including vector databases (e.g., Pinecone, Weaviate, ChromaDB) and advanced retrieval techniques for grounding agents in enterprise data.
- Awareness of Large Language Models (LLMs) (e.g., OpenAI, Gemini, Claude, Llama) and their practical application in data engineering contexts such as automated schema inference, natural language querying, and intelligent data cataloging.
Education:
- Bachelor’s degree/University degree or equivalent experience
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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.------------------------------------------------------
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Skills Required
- 5+ years experience in Big Data or enterprise large-scale application development using Databricks, Scala, Java, and the Python ecosystem
- Deep understanding of data modeling, data warehousing concepts, methodologies, and best practices
- Strong experience with data processing technologies (Spark, Starburst, Snowflake, Redshift)
- Experience with storage technologies (Hadoop, MongoDB, Oracle) and large database systems
- Experience with ETL and orchestration tools (Airflow, Ab Initio, Talend)
- Experience building data lakes, data warehouses, data mesh architectures, and data pipelines (ETL vs ELT)
- Strong expertise in Starburst/Trino, data federation, and data virtualization architectures
- Experience designing and developing distributed systems handling structured and unstructured data
- Proficiency in PL/SQL and Unix Shell scripting; strong SQL and database tuning experience
- Experience designing OLTP, Operational Data Store (ODS), and Data Warehouse applications
- Experience with data quality and data lineage
- Demonstrable experience with AI agents and their integration into data engineering workflows
- Practical knowledge of Model Context Protocol (MCP) servers including setup, configuration, and tool interfaces
- Understanding of Retrieval-Augmented Generation (RAG) principles and vector databases (Pinecone, Weaviate, ChromaDB)
- Awareness of Large Language Models (OpenAI, Gemini, Claude, Llama) and their application in data engineering
- Bachelor's degree or equivalent experience
- Experience with real-time data processing and data virtualization technologies such as Flink
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
What We Do
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