
AWS Data Engineer- Sagemaker
4 weeks ago
Primary skillsets : AWS services including Glue, Pyspark, SQL, Databricks, Python
Secondary skillset : Any ETL Tool, Github, DevOPs(CI-CD)
Mandatory Skill Set:
- Python, PySpark , SQL, AWS with Designing, developing, testing and supporting data pipelines and applications
- Strong understanding and hands-on experience with AWS services like EC2, S3, EMR, Glue, Redshift
- Strong in developing and maintaining applications using Python and PySpark for data manipulation, transformation, and analysis
- Design and implement robust ETL pipelines using PySpark, focusing on performance, scalability, and data quality
- Lead and manage projects, including planning, execution, testing, and documentation and handling customer interacation as key point of contact
- Translate business requirements into technical solutions using AWS cloud services and Python/PySpark
- Deep understanding of Python and its data science libraries, along with PySpark for distributed data processing
- Proficiency in PL/SQL, T-SQL for data querying, manipulation, and database interactions
- Excellent written and verbal communication skills to collaborate with team members and stakeholders
- Experience leading and mentoring teams in a technical environment and providing proposals on solutioning and designed based approach
- 5+ years working experience in data integration and pipeline development.
- 5+ years of Experience with AWS Cloud on data integration with a mix of Apache Spark, Glue, Kafka, Kinesis, and Lambda in S3 Redshift, RDS, MongoDB/DynamoDB ecosystems Databricks, Redshift experience is a major plus
- 3+ years of experience using SQL in related development of data warehouse projects/applications (Oracle & amp; SQL Server)
- Strong real-life experience in python development especially in PySpark in AWS Cloud environment
- Strong SQL and NoSQL databases like MySQL, Postgres, DynamoDB, Elasticsearch Workflow management tools like Airflow
Good to Have : Snowflake, Palantir Foundry
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