Tech Mahindra - Azure Data Engineer - ETL (3-7 yrs)

Primary Skills : Azure Data Engineer / Senior Azure Data Engineer

Experience : 3-7 years

Notice Period : Immediate Joiners - 30 Days

Location : Bangalore, Pune, Chennai, Noida

Work shifts : 5 days' work from Office

- Should be ready to work in afternoon shift (2 pm IST to 11 pm IST) and WFO (as per TechM policy).

- Bachelor's degree in computer science, Computer Engineering, or related technical discipline.

- 4+ years of IT experience and must have worked in DW project for at least 3 years

- Worked in atleast 3 Data Warehousing projects by contributing in developing ETL solution.

- Proficient with Python, Pyspark for data manipulation, analysis, and extraction

- Advance SQL : Should be very strong understanding of Database & Data Modelling. Must posses hands-on experience in writing complex SQLs

- Azure Data Factory (ADF) : A key service for data integration and orchestration. Should know how to create data pipelines, schedule activities, and manage data movement and transformation using ADF

- Azure Databricks : A cloud-based big data analytics platform based on Apache Spark. Should be adept at using Databricks for data engineering tasks like data ingestion, transformation, and analysis

- Azure SQL Database : Microsoft's fully managed relational database service. Should be proficient in using it for data storage, retrieval, and basic manipulation.

- Azure DevOps : Knowledge of Azure DevOps is valuable for implementing Continuous Integration and Continuous Deployment (CI/CD) pipelines for data engineering solutions

- Monitoring and Optimization : Understanding how to monitor the performance of data engineering solutions and optimize them for better efficiency is crucial

- Data Quality and Data Cleaning : Knowing how to ensure data quality and perform data cleaning operations to maintain reliable data is important for data engineers.

- Data Modeling and ETL/ELT : You should be skilled in data modeling techniques and Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes for data integration.

- Good to have Apache Spark : Understanding of Spark and its various components. Spark is a fast and general-purpose data processing engine that can run in-memory, making it well-suited for iterative algorithms and interactive data analysis.