Responsibilities :
- Proven success in 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 owners to define key business requirements and convert to user stories with required technical specifications.
- Communicate results and business impacts 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 for supported area.
- Design and create fault-tolerance data pipelines running 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 :
0-6months of hands-on experience Designing and developing Data Pipelines for Data Ingestion or Transformation using Python (PySpark) / Spark SQL in AWS cloudExperience 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 EnvironmentsExperience in debugging, tunning and optimizing PySpark data pipelinesShould 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 implementationsExperience with building data pipelinesin streaming and batch mode.Experience with Git and CI / CD pipelines to deploy cloud applicationsGood knowledge of designing Hive tables with partitioning for performance.Desired Qualification :
Experience in data modellingHands on creating workflows on any Scheduling Tool like Autosys, CA Workload AutomationProficiency in using SDKsfor interacting with native AWS servicesStrong understanding of concepts of ETL, ELT and data modeling.Skills Required
Advanced Sql, Numpy, Python, Pyspark, Shell Scripting, Data Modelling