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