Data Engineer-Assistant Vice President

Discover your future at Citi

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Job Overview

The Data Engineer is accountable for developing high quality data products to support the Bank’s regulatory requirements and data driven decision making. A Data Engineer will serve as an example to other team members, work closely with customers, and remove or escalate roadblocks. By applying their knowledge of data architecture standards, data warehousing, data structures, and business intelligence they will contribute to business outcomes on an agile team.

Responsibilities

•Developing and supporting scalable, extensible, and highly available data solutions

•Deliver on critical business priorities while ensuring alignment with the wider architectural vision

•Identify and help address potential risks in the data supply chain

•Follow and contribute to technical standards

•Design and develop analytical data models

Required Qualifications & Work Experience

•First Class Degree in Engineering/Technology (4-year graduate course)

•8 to 11 years’ experience implementing data-intensive solutions using agile

methodologies

•Experience of relational databases and using SQL for data querying, transformation and manipulation

•Experience of modelling data for analytical consumers

•Ability to automate and streamline the build, test and deployment of data pipelines

•A passion for learning new technologies, and a desire for personal growth, through self-study, formal classes, or on-the-job training

•Excellent communication and problem-solving skills

•An inclination to mentor; an ability to lead and deliver medium sized components independently

Technical Skills (Must Have)

•ETL: Hands on experience of building data pipelines. Proficiency in data

integration platform Apache Spark

•Big Data: Experience of ‘big data’ platforms such as Hadoop, Hive or Snowflake for data storage and processing

•Data Warehousing & Database Management: Expertise around Data

Warehousing concepts, Relational (Oracle, MSSQL, MySQL) and NoSQL (MongoDB, DynamoDB) database design

•Data Modeling & Design: Good exposure to data modeling techniques; design,

optimization and maintenance of data models and data structures

•Languages: Proficient in one or more programming languages commonly used in data engineering such as Python, Java or Scala along with good knowledge on Unix shell scripting

•DevOps: Exposure to concepts and enablers - CI/CD platforms, version control,

automated quality control management

•Data Governance: A strong grasp of principles and practice including data quality, security, privacy and compliance

•Scheduling Tool: Experience on Autosys and Cron

•Data Quality & Controls: Exposure to data validation, cleansing, enrichment and

data controls

•File Formats: Exposure in working on Event/File/Table Formats such as Avro,

Parquet, Protobuf, Iceberg, Delta

Technical Skills (Valuable)

•Java, Spring boot and Maven: Exposure to Spring Boot, Maven, RESTful API

and microservices

•Cloud: Good exposure to public cloud data platforms such as S3, Snowflake,

Redshift, Databricks, BigQuery, etc. Demonstratable understanding of underlying architectures and trade-offs

•Containerization: Fair understanding of containerization platforms like Docker,

Kubernetes

Certification on any one or more of the above topics would be an advantage