Senior Data Engineer with 12+ Years

Role : Data Engineer Location : New York (100% onsite) (need local to NY) Interview mode : ( 1 Video interview and CI will be in person) Candidate need to have colab set up ready with Gmail account so they can code on the L1 interview Must have : Languages & Scripting: Spark, Python, Java, Scala, Hive, Kafka, SQL Cloud Platforms: AWS Data Warehousing & Analytics: Redshift or Snowflake or Big Query Data Integration & ETL: AWS Glue, Aws EMR, Spark, Data Bricks CI/CD: AWS Code Pipeline, Jenkins, CloudFormation, Docker, Kubernetes JD : • Results-driven Data Engineer with a decade of expertise in Data engineering across cloud platforms with a total of 12 years in IT. • Extensive experience utilizing Google Cloud Platform (GCP) services, including BigQuery, Dataflow, Dataprep, and Pub/Sub, for data engineering solutions. • Proficient in building and managing GCP data pipelines with tools like Cloud Composer and Cloud Dataflow. • Proven ability in developing and deploying applications on Google Kubernetes Engine (GKE). • Strong background in implementing security and compliance on GCP, ensuring data privacy and regulatory adherence. • Track record of optimizing cost and resource usage within GCP environments. • Skilled in AWS services such as Amazon EMR, Redshift, and Glue for efficient data processing. • Expertise in architecting scalable, cost-effective solutions on AWS, with proficiency in configuring AWS Lambda for serverless computing. • Adept at setting up AWS Kinesis streams to process real-time data, enhancing system responsiveness and data-driven decision-making. • Proficient in leveraging AWS DynamoDB to create scalable, low-latency NoSQL databases for dynamic applications. • Deep expertise in optimizing and managing Amazon Redshift data warehouses to deliver high-performance analytics and business insights. • Experienced in integrating AWS services into CI/CD pipelines, streamlining automation for continuous integration, delivery, and deployment. • Skilled in setting up and securing AWS Virtual Private Cloud (VPC) environments. • Proficient in managing Azure virtual machines (VMs) for cloud infrastructure operations. • Extensive experience managing on-premises data infrastructure, including data warehouses and databases. • Familiar with AWS DevOps practices for continuous integration and deployment. • Expertise in using Git for version control in DBT projects, ensuring proper tracking and documentation of data model changes. • Skilled in performance optimization and tuning of on-premises data systems. • Proficient in data migration strategies between on-premises and cloud environments. • Strong troubleshooting skills in resolving issues within on-premises data systems. • Proven ability to maintain high availability and disaster recovery solutions in on-premises environments. • Experienced in implementing CI/CD pipelines using tools like Jenkins and GitLab CI/CD. • Adept in automated testing processes, including unit, integration, and regression testing. • Skilled in gathering and analyzing project requirements to ensure alignment with business goals. • Experienced in Agile project management, contributing to successful outcomes through data-driven analytics and collaborative teamwork