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Data Engineering Lead
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Cognizant is seeking a highly skilled Data Engineering Lead to join our team. As a key member of our organization, you will be responsible for designing and building efficient data pipelines to attain a modern future state platform for collections operations.
Key Responsibilities- Manage data engineering initiatives, overseeing the development of pipelines that are dependable, optimized, verifiable, and sustainable.
- Take charge of data engineering initiatives to ensure the dependability, effectiveness, testability, and sustainability of pipelines.
- Collaborate with business representatives, data analysts, and software developers to comprehend data requirements and devise strategies to fulfill those requirements.
- Perform data encryption to ensure data masking of sensitive customer information using Spark and ingesting large sizes of data files into Hive, HBase, and Phoenix tables.
- Perform batch processing on Hive tables with Spark using Python to merge and transform large volumes of data.
- Aid in creating data governance protocols and processes aimed at safeguarding customer personal information, ensuring business compliance with governmental regulations and security benchmarks.
- Improve the overall Code quality and Code reliability by automated functional and unit testing, thus enabling early detection of bugs and defects.
- Use CI/CD tools like Azure DevOps to build, test, and deploy the code and automated job schedules.
- Develop Python and shell scripts to perform load assurance tests on ingestion and transformations.
- Provide guidance and support to new joiners on the best Data Engineering practices.
- Perform code reviews and offer input regarding the design and execution of the code through peer reviews.
- Perform System Testing across multiple sources to identify errors and supporting bug fixes in production releases.
- Solid experience on design patterns to implement strategic, tactical, and operational products spanning across all disciplines of Business Intelligence and Analytics, including data ingestion, integration, modeling, performance acceleration, visualization, etc.
- Solid functional understanding of Open-Source Big Data Technologies.
- Experience working on Spark Streaming.
- Preferable work experience with Hortonworks distribution and Azure cloud platform.
- Proficient with HDFS, Hive, HBase, Spark, and SQL.
- Experience in Azure cloud platform and services, including ADLS2, HD Insight, Azure SQL, Azure Data Factory, Cosmos DB, Azure Blob Storage, and Azure Databricks.
- Expertise in various connectors and pipelines for batch and real-time data collection/delivery.
- At least 2+ years of experience in NoSQL databases, such as HBase, MongoDB, and Phoenix.
- Must have strong experience with code management and build tools, such as Git, Bitbucket, GitHub, GitLab, SVN, Maven, Gradle, and SBT.
- Must have hands-on experience with CI/CD deployment tools, such as Jenkins, Bamboo, and Azure DevOps.
- Proficient in relevant programming languages, such as Java, Scala, Python, and SQL.
- Proficiency in analyzing business requirements for data architecting with both SQL/NoSQL databases.
- Performance tuning - table partitioning and indexing, process threading.
- Support multiple Agile Scrum teams with planning, scoping, and creation of technical solutions for new product capabilities, through to continuous delivery to production.
