
Data Engineer
1 week ago
Key responsibilities include:
- Work closely with Product Owners and AWS Professional Service Architects to understand requirements, formulate solutions, and implement them.
- Implement scalable data transformation pipelines as per design - Implement Data model and Data Architecture as per laid out design.
- Evaluate new capabilities of AWS analytics services, develop prototypes, and assist in drawing POVs, participate in design discussions
Requirements:
- Minimum 3 years experience implementing transformation and loading of data from a wide variety of traditional and non-traditional sources such as structured, unstructured, and semi structured using SQL, NoSQL and data pipelines for real-time, streaming, batch and on-demand workloads
- At least 2 years implementing solutions using AWS services such as Lambda, AWS Athena and Glue AWS S3, Redshift, Kinesis, Lambda, Apache Spark,
- Experience working with data warehousing data lakes or Lakehouse concepts on AWS
- Experience implementing batch processing using AWS Glue/Lake formation, & Data Pipeline
- Experience in EMR/MSK
- Experience or Exposure to AWS Dynamo DB will be a plus
- Develop object-oriented code using Python, besides PySpark, SQL and one other languages (Java or Scala would be preferred)
- Experience on Streaming technologies both OnPrem/Cloud such as consuming and producing from Kafka, Kinesis
- Experience building pipelines and orchestration of workflows in an enterprise environment using Apache Airflow/Control M
- Experience implementing Redshift on AWS or any one of Databricks on AWS, or Snowflake on AWS
- Good understanding of Dimensional Data Modelling will be a plus.
- Ability to multi-task and prioritize deadlines as needed to deliver results
- Ability to work independently or as part of a team
- Excellent verbal and written communication skills with great attention to detail and accuracy
- Experience working in an Agile/Scrum environment
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