ETL Developer

Responsibilities:

• Data Profiling, Scorecards and Understanding data gaps and Interaction with Data Stewards and Business SME’s and apply Business and Data transformation rules.
• Data Cleansing, Data Validation, Data Standardization, Customer Address Validation using Address Doctor and migrate the cleansed data to Salesforce AWS3 Buckets, Data Platform HIVE DB and Mule Soft, downstream applications using Informatica Power Center under AWS and Informatica Intelligence Cloud Services.
• Preparation of ETL Technical specifications and identification of GAP's and get it reviewed by Data Architect's, Business SME's and proper sign off and developer handover.
• Using Informatica Power Exchange connector to read DB2 Mainframe Sales system data and load into Salesforce (Sales & Marketing Clouds) using Salesforce Connector.
• Bulk Data loads and Deletion in Salesforce objects by using automated informatica ETL Jobs.
• Understanding GDW Teradata BTEQ scripts and enhancements based on new requirements and providing required Salesforce objects names and its data types under change list of Salesforce Agile accelerator and interaction with Salesforce team.
• ETL code development, Unit testing code peer review by Data Architects and proper hand over to QA team and then code deployment across the environments. Preparation of Technical design document. Post production and warranty.

 Requirement:

• Expert designer, coder and tester for data warehouse programming by using Informatica Power Center, Informatica Developer Tool, Informatica Power Exchange.
• Experience in Data profiling, Data Cleansing and Standardization and building reusability logic.
• Able to Read/Write data from/to different DB’s and applications like – Teradata, SQL Server, DB2, Oracle, Salesforce, AWS S3, DP Hive.
• Experience in design reviews and extensive documentation of standards, best practices, and ETL procedures.
• Evaluate all functional requirements and map documents and perform troubleshoot on all development processes.

Key Skills:

• SQL competence.
• Understanding of data modeling concepts.
• Knowledge of at least one ETL tool.
• Knowledge of different SQL/NoSQL data storage techniques and Big Data technologies.