Senior Big Data Engineer Job at Citi (Pune)

Job Description

Senior Big Data Engineer - Assistant Vice President 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.

Job Responsibility

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

Requirements

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
Experience in cloud native technologies and patterns
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
ETL: Hands on experience of building data pipelines. Proficiency in two or more data integration platforms such as Apache Spark, Talend and Informatica
Cloud: Hands on experience on Cloud preferably AWS
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
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

Nice to have

Ab Initio: Experience developing Co>Op graphs
ability to tune for performance. Demonstrable knowledge across full suite of Ab Initio toolsets e.g., GDE, Express>IT, Data Profiler and Conduct>IT, Control>Center, Continuous>Flows
Cloud: Good exposure to public cloud data platforms such as AWS, S3, Snowflake, Redshift, Databricks, BigQuery, etc. Demonstratable understanding of underlying architectures and trade-offs
Data Quality & Controls: Exposure to data validation, cleansing, enrichment and data controls
Containerization: Fair understanding of containerization platforms like Docker, Kubernetes
File Formats: Exposure in working on Event/File/Table Formats such as Avro, Parquet, Protobuf, Iceberg, Delta
Others: Experience of using a Job scheduler e.g., Autosys. Exposure to Business Intelligence tools e.g., Tableau, Power BI
Certification on any one or more of the above topics would be an advantage

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Senior Big Data Engineer

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