Senior Data Engineer

Job Details :Position : Senior Data Engineer - AWSExperience : 10+ Years (5 Years hands-on AWS Data Engineering)Location : Mumbai (Remote)Required Skills : AWS (S3, Glue, Lambda, & RedShift), ETL, Data Quality & Data Modeling (Conceptual)Role Overview :- A Senior Data Engineer who understands client requirements, develops and delivers the analytical solutions as per the scope. - The role requires good business understanding, ETL development, data modeling and API development experience on AWS.Job Responsibilities :- Design and development of data solution including data lake, data marts and other data solutions to support the analytics needs of the organization- Apply best practices during design in data modeling (logical, physical) and ETL pipelines (streaming and batch)- Design, develop and manage the pipelining (collection, storage, access), Data Engineering (data quality, ETL, Data Modeling) and understanding (documentation, exploration) of the data;- Interact with stakeholders regarding data availability and report format/presentation, to ensure the report provides the information the stakeholders need without compromising the report's accuracy; - Must be able to manage small team of technical developers to deliver the end -to-end solutions. Required Skills :- Hands on experience with AWS data components is required (e.g., S3, Redshift, Athena, EMR, Kinesis and Glue)- The ability to design, implement and use effective database structures to ensure data is organised effectively and ready for downstream development of data science and analytics- Experience working in a structured environment and desire for good documentation, exceptional delivery, effective organisation, and high -quality communication- Strong SQL and data profiling skills- Hands-on ELT experience using tools like Streams and Tasks or DBT- Familiarity with a traditional BI application such as Power BI, Tableau or Spotfire- Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.- Solid understanding of data warehousing principles, concepts and best practices (e.g. ODS, Data Mart, Data Lakes, Data Vault); (ref:hirist.com)