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Big Data Engineer - C2C - NYC Hybrid (3 days a week) at NYC, New York, USA
Email: [email protected]
From:

Dharmasastha,

Vysystems

[email protected]

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Technical Skills:

Big Data Ecosystem:   HDFS, Hive, Oozie, Sqoop, Impala, Presto and Spark.

Cloud Ecosystem:       AWS (EMR, S3, Athena), Snowflake

Languages:                   Python, SQL and Shell Scripting.

Databases:                   MySQL, Teradata, and SQL SERVER.

Orchestration/Tools:   Oozie, Airflow, CTRL-M, Maven, Jenkins, IntelliJ, GIT

JD

Expertise in all components of Big Data Ecosystem- Spark, Hive, Sqoop, Oozie, Impala and Hue

Extensive Experience with AWS services ensuring efficient data management and analysis.

Successfully led the migration of legacy systems to Datawarehouse environments and Cloud, utilizing Informatica, Snowflake and AWS technologies, resulting in improved scalability, performance, and cost-effectiveness.

Extensively worked with lambda, S3, cloudwatch,SQS,SNS,Step Functions and IAM roles in AWS services.

Extensive knowledge on data serialization techniques like Avro, Sequence Files, Parquet, JSON and ORC.

Experienced in handling databases: MySQL, SQL Server, Teradata.

Expertise working across all phases of SDLC requirements gathering, system design, development, enhancement, maintenance, testing, deployment, production support, and documentation.

Working experience in creating complex data ingestion pipelines, data transformations, data management and data governance in a centralized enterprise data hub.

Knowledge of Kubernetes ecosystem.

Did Data analysis and interpretation Using Databricks features to perform data transformations, run queries, and build visualizations collaborating Working with diverse teams to deliver insights in Analytics platform.  good to have.

Strong problem-solving skills

Strong team player with good communication, analytical, presentation and inter-personal skills.

Keywords: sthree
Big Data Engineer - C2C - NYC Hybrid (3 days a week)
[email protected]
[email protected]
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Thu Nov 07 03:04:00 UTC 2024

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