Data Engineer

Role & responsibilities

Must Haves Skills

Extensive experience as Data Engineer with Python Language and Cloud Technologies (AWS preferably).

Experience in Automating ETL process/Pipelines and AWS Data & Infrastructure with Python.

Extensive experience with AWS components like S3, Athena, EMR, Glue, Redshift, Kinesis and SageMaker.

Extensive Experience with SQL/Unix/Linux scripting.

Developing/testing Experience on Cloud/On Prem ETL Technologies (Ab Initio, AWS Glue, Informatica, Alteryx).

Experience in Data migration from Onprem to Cloud is Plus.

Experienced in large-scale application development testing Cloud/ On Prem Data warehouse, Data Lake, Data Science.

Extensive experience in DevOps/Data Ops space.

Having experience in Data Science platforms like SageMaker/Machine Learning Studio/ H2O is plus.

Work Description SDET – Python, AWS, Unix and ETL.

• Work with business stakeholders, Business Systems Analysts and Developers to ensure delivery of Data Applications.

• Building Automation Frameworks using Python.

• Designing and managing the data workflows using Python during development and deployment of data products

• Design, development of Reports and dashboards.

• Analyzing and evaluating data sources, data volume, and business rules.

• Should be well versed with the Data flow and Test Strategy for Cloud/ On Prem ETL Testing.

• Interpret and analyses data from various source systems to support data integration and data reporting needs.

• Experience in testing Database Application to validate source to destination data movement and transformation.

• Work with team leads to prioritize business and information needs.

• Develop and summarize Data Quality analysis and dashboards.

• Knowledge of Data modeling and Data warehousing concepts with emphasis on Cloud/ On Prem ETL.

• Execute testing of data analytic and data integration on time and within budget.

• Troubleshoot & determine best resolution for data issues and anomalies

• Has deep understanding of data architecture & data modeling best practices and guidelines for different data and analytic platforms