About the role
Location: Mumbai
Experience: 4yrsto 8yrs
Technologies / Skills: Advanced SQL, Python and associated librarieslike Pandas, Numpy etc., Pyspark , Shell scripting, DataModelling, Big data, Hadoop, Hive, ETL pipelines.
Responsibilities:
• Proven successin communicating with users, other technical teams, and senior management to collect requirements, describe data modeling decisions and develop data engineering strategy.
• Ability to work with business ownersto define key businessrequirements and convert to user stories with required technical specifications.
• Communicate results and businessimpacts of insight initiatives to key stakeholders to collaboratively solve business problems.
• Working closely with the overall Enterprise Data & Analytics Architect and Engineering practice leads to ensure adherence with the best practices and design principles.
• Assures quality, security and compliance requirements are met forsupported area.
• Design and create fault-tolerance data pipelinesrunning on cluster
• Excellent communication skills with the ability to influence client business and IT teams
• Should have design data engineering solutions end to end. Ability to come up with scalable and modular solutions
Required Qualification:
• 3+ years of hands-on experience Designing and developing Data Pipelinesfor Data Ingestion or Transformation using Python (PySpark)/Spark SQL in AWS cloud
• Experience in design and development of data pipelines and processing of data at scale.
• Advanced experience in writing and optimizing efficient SQL queries with Python and Hive handling Large Data Sets in Big-Data Environments
• Experience in debugging, tunning and optimizing PySpark data pipelines
• Should have implemented concepts and have good knowledge of Pyspark data frames, joins, caching, memory management, partitioning, parallelism etc.
• Understanding of Spark UI, Event Timelines, DAG, Spark config parameters, in order to tune the long running data pipelines.
• Experience working in Agile implementations
• Experience with building data pipelinesin streaming and batch mode.
• Experience with Git and CI/CD pipelines to deploy cloud applications
• Good knowledge of designing Hive tables with partitioning for performance.
Desired Qualification:
• Experience in data modelling
• Hands on creating workflows on any Scheduling Tool like Autosys, CA Workload Automation
• Proficiency in using SDKsfor interacting with native AWS services
• Strong understanding of concepts of ETL, ELT and data modeling.