Sr Technical Lead-Data Engg

Area(s) of responsibility

Data engineer-GCP

1. Extracting and Migrating Data from Various Data Sources:

• Extracting Data: This involves pulling data from different sources such as databases, APIs, flat files, or cloud storage. The goal is to gather all relevant data needed for analysis or application use.
• Migrating Data: This refers to transferring data from one system to another, ensuring that the data remains intact, accurate, and accessible. This process often includes data cleaning, transformation, and loading (ETL).

2. Coordinating with Various Teams for Data Management in Projects:

• Team Collaboration: You will work closely with different teams, such as data analysts, data scientists, and IT staff, to ensure that data is managed effectively throughout the project lifecycle.
• Data Governance: Ensuring that data policies and standards are followed, maintaining data quality, and managing data access and security.

3. Coordinating with Business to Understand Business Data for Application Management:

• Business Liaison: Acting as a bridge between the technical team and business stakeholders to understand their data needs and requirements.
• Data Requirements Gathering: Collecting and documenting business data requirements to ensure that the application meets business objectives.

4. Coordinating with Infrastructure Team for Infrastructure Provisioning in Application:

• Infrastructure Coordination: Working with the infrastructure team to ensure that the necessary hardware and software resources are available for the application.
• Provisioning: Setting up and configuring servers, databases, and other infrastructure components needed for the application to run smoothly.

5. Technically Leading Application Team for Various Technical Activities:

• Technical Leadership: Providing guidance and direction to the application team on technical matters, ensuring that best practices are followed.
• Problem Solving: Addressing technical challenges and finding solutions to ensure the successful delivery of the application.

6. Business Process Understanding of the Application:

• Process Analysis: Understanding the business processes that the application supports, identifying areas for improvement, and ensuring that the application aligns with business goals.
• Process Optimization: Working to streamline and optimize business processes through the application.

7. Spark and Scala Experience

8. BigQuery, DataProc, DataFlow, and Google Cloud in Data Engineering

• BigQuery
• DataPro, DataFlow: 

9. Python Libraries for Data Analytics & Engineerin- Pandas, NumPy, Libraries,SciPy, Matplotlib & Seaborn AND Scikit-learn