Data Engineer – C10/Officer (India)
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)
- 3 to 4years’ 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
echnical Skills (Must Have)
- ETL: Hands on experience of building data pipelines. Proficiency inat least one of thedata integration platforms such as Apache Spark, Talend and Informatica
- Big Data:Exposure to‘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
- DevOps : Exposure to concepts and enablers - CI/CD platforms, version control, automated quality control management
Technical Skills (Valuable)
- 3-5 Years of Apache Spark /Pyspark : experience in using Apache Pyspark to develop scalable and efficient data processing applications. Experienced in writing Pyspark code to handle large data set ,perform data transformation , familiarity with Pyspark integration with other Apache Spark component ,such as Spark SQL , Understanding of Pyspark optimization techniques such as caching, partitioning and broadcasting
- 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 : Basics of Job scheduler like Autosys. Basics of Entitlement management
Certification on any of the above topics would be an advantage.
Applications Development
Time Type:
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