Hands-on experience with building enterprise-grade products with at least one of the following Cloud technologies - Google Cloud Platform (GCP), Amazon Web Services (AWS), Microsoft Azure.

Proven track record coding with at least one programming language e.g., Scala (preferred), Python Expert in cloud computing platforms and offerings from Google Cloud Platform (GCP) (preferred), Microsoft Azure.

Design, develop, and deploy scalable data pipelines on cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP) Hands-on experience with cloud platforms such as AWS, Azure, or GCP, including services like S3, Redshift, RDS, Azure SQL Database, BigQuery, etc.

Hands-on experience with cloud data platforms (e.g., AWS Redshift, Google BigQuery, Azure Synapse) and data processing frameworks (e.g., Apache Spark, Kafka.

Hands-on experience with data flow and pipeline frameworks such as Kafka, NiFi, Spark, Airflow, and Hadoop. Hands-on experience with one or more database technologies (e.g., PostgreSQL, Oracle, MySQL, SQL Server, DB2, MongoDB, Neo4j, Couchbase, Cassandra.

Hands-on experience with Java, Spring Boot, Cloud (Azure/GCP/AWS), Kafka (or other messaging service), Memcached/Redis or any caching technologies, relational DBMS (MySQL, Oracle etc.

The ideal candidate will have extensive knowledge and hands-on experience in using Apache Spark, Scala, and Google Cloud Platform (GCP). - Utilize Google Cloud Platform (GCP) for the deployment, operation, and monitoring of large-scale data processing systems.

You will have deeper understanding of Data Engineering approaches along with hands on experience in building highly scalable solutions. Expert in cloud computing platforms and offerings from Google Cloud Platform (GCP) (preferred), Microsoft Azure.

Integrate DevOps/SRE practices on the data team by solving for performance, monitoring, and alerting. The Senior Data Engineer will ensure an optimal delivery of initiatives across Data Pipeline Design and Development, Data Warehousing and Modeling, and Performance Optimization.

2 years of experience implementing and maintaining datasets in relational databases such as PostgreSQL on AWS (RDS) or Google Cloud (Cloud SQL) 1 years of experience designing, implementing, tuning, and maintaining datasets in OpenSearch / ElasticSearch on AWS or Google Cloud.

Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud. 3+ yrs of proven experience as a data engineer or similar role, with a strong background in data warehousing, ETL/ELT processes, and data modeling.

As a part of our Big Data Product team, the Data Solutions Engineer will be responsible for developing and validating Big data products and applications which runs on the large Hadoop cluster and Cloud.

The qualified engineer will be developing and testing ETL process, Migrating different applications to cloud , developing Data validation tools used for performing quality assessments and measurements on different data sets that feed data & AI products.

Experience in data engineering, preferably in Google Cloud Platform with BigQuery. Hands-on experience in designing, building, testing and deploying data pipelines in Teradata and Hadoop platform with experience in, HDFS, Hive, Spark, Streaming, HBase, Kafka, Oozie etc.

Hands on experience in dashboard development using Looker/Tableau. Performing data migration and conversion activities on different applications and platforms. Participating in discussion for technical architecture, data modeling and ETL standards, collaborate with Product Managers, Architects and Senior Developers to establish the physical application framework (e.g. libraries, modules, execution environments.