Aws data engineer- sagemaker

4 days ago

Bangalore, India YASH Technologies Full time

Primary skillsets : AWS services including Glue, Pyspark, SQL, Databricks, Python Secondary skillset : Any ETL Tool, Github, Dev OPs(CI-CD) Mandatory Skill Set: Python, Py Spark , 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 Py Spark for data manipulation, transformation, and analysis Design and implement robust ETL pipelines using Py Spark, 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/Py Spark Deep understanding of Python and its data science libraries, along with Py Spark 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, Mongo DB/Dynamo DB 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 Py Spark in AWS Cloud environment Strong SQL and No SQL databases like My SQL, Postgres, Dynamo DB, Elasticsearch Workflow management tools like Airflow Good to Have : Snowflake, Palantir Foundry

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