The Data Analytics Intmd Analyst is a developing professional role in the field of Data engineering. The Data Engineer is accountable for developing high quality data products to support the Bank’s Regulatory and In-Business requirements and data driven decision making. 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. Deals with most problems independently and has some latitude to solve complex problems. Applies analytical thinking and knowledge of data analysis tools and methodologies. Breaks down information in a systematic and communicable manner. Developed communication and diplomacy skills are required in order to exchange potentially complex/sensitive information. Moderate but direct impact through close contact with the businesses’ core activities. Quality and timeliness of service provided will affect the effectiveness of own team and other closely related teams.

QUALIFICATIONS:

• 2-3 years of experience implementing data-intensive solutions using agile methodologies.
• 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

TECHNICAL SKILLS (MUST HAVE):

• ETL: 2-3 years of Hands on experiencing in building data pipelines using Apache Spark.
• Big Data: Hands on experience of working on “Big Data” platforms such as Hadoop, Hive or Snowflake for data storage and processing.
• Data Warehousing & Database Management: Understanding of 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
• DevOps: Exposure to concepts and enablers - CI/CD platforms, version control, automated quality control management

TECHNICAL SKILLS (NICE TO HAVE):

• Data Quality & Controls: Exposure to data validation, cleansing, enrichment and data controls
• Containerization: Understanding of containerization platforms like Docker, Kubernetes
• File Formats: Exposure in working on Event/File/Table Formats such as Avro, Parquet, Iceberg
• Others: Basics of Job scheduler like Autosys. Basics of Entitlement management
• Certification on any of the above topics would be an advantage.
  Education:
  Bachelor’s/University degree or equivalent experience
  This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.
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MOST RELEVANT SKILLS

Please see the requirements listed above.
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OTHER RELEVANT SKILLS

For complementary skills, please see above and/or contact the recruiter.
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• 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
• Considers the business implications of the application of technology to the current business environment; identifies and communicates risks and impacts.
• Drives communication between business leaders and IT; exhibits sound and comprehensive communication and diplomacy skills to exchange complex information.
• Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm’s reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency. Integrates in-depth data analysis knowledge with a solid understanding of industry standards and practices.