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Job Overview

As a Senior Data Engineer, you will lead a team of big data engineers in developing architectures that meet the needs of clients and team members. You will provide technical leadership and guidance to your team.

The ideal candidate will be responsible for building pipelines to bring in a wide variety of data from multiple sources within the organization as well as from social media and public data sources.

Collaboration with cross-functional teams is key to sourcing data and making it available for downstream consumption.

Effective solution design will ensure successful project execution.

Regular communication with key stakeholders is crucial to understand any key concerns or risks/issues.

The successful candidate will have excellent problem analysis skills and experience working with Cloud Infra providers such as Azure/AWS.

Apache Spark (Spark SQL, Dataframe API) or Hive query language (HQL) experience is required for building batch processing data pipelines.

Additionally, experience in building streaming data pipeline using Apache Spark Structured Streaming or Apache Flink on Kafka & Delta Lake is essential.

Knowledge of NOSQL databases, particularly Cosmos DB, Restful API's and GraphQL, is beneficial.

A strong understanding of Big data ETL processing tools, Data modelling and Data mapping is also required.

Hive and Hadoop file formats (Avro / Parquet / ORC) are commonly used by the team.

Basic knowledge of scripting (shell / bash) and experience with multiple data sources including relational databases (SQL Server / Oracle / DB2 / Netezza), NoSQL / document databases, flat files are necessary.

Familiarity with CI/CD tools such as Jenkins, JIRA, Bitbucket, Artifactory, Bamboo and Azure Dev-ops is beneficial.

DevOps practices using Git version control are also important.

The ability to debug, fine tune and optimize large scale data processing jobs is critical.