Job Title : Data Engineer

Location : Hyderabad (Work from Office)

Experience : 5+ Years

Notice Period : Immediate / Last Working Day till 31st August (Serving Notice Preferred)

About the Role :

We are looking for an experienced Data Engineer to design, develop, and optimize scalable data pipelines and cloud-based big data solutions, preferably on Microsoft Azure. The ideal candidate will have strong expertise in ETL development, big data platforms, and performance optimization of large-scale datasets. This role demands hands-on technical skills with tools like Informatica, Apache Hive, HDFS, and Azure Data Lakehouse architectures, along with experience in scheduling and orchestration tools.

Key Responsibilities :

- Design, build, and maintain scalable and reliable data pipelines for analytics and business needs.

- Develop and optimize ETL workflows using Informatica.

- Work with big data platforms such as Apache Hive and HDFS for distributed data processing.

- Manage and optimize data storage solutions with Azure Data Lake and Lakehouse architectures.

- Collaborate with cross-functional teams to ensure data quality, governance, and security compliance.

- Implement performance tuning, data partitioning, and query optimization strategies for large datasets.

- Use workflow orchestration and scheduling tools such as Airflow, Control-M, or Azure Data Factory.

- Support DevOps and CI/CD practices for data pipelines and workflows.

Required Skills & Experience :

- 5+ years of experience as a Data Engineer with hands-on expertise in :

1. Apache Hive, HDFS, and distributed data processing.

2. ETL development (preferably with Informatica).

3. Relational databases and data modeling for structured and semi-structured datasets.

4. Azure Data Lake and Lakehouse architecture.

- Strong knowledge of performance tuning and optimization for large-scale data solutions.

Preferred Skills :

- Experience with scheduling and orchestration tools (Airflow, Control-M, Azure Data Factory).

- Familiarity with data quality frameworks, governance practices, and cloud security standards.

- Exposure to CI/CD pipelines and DevOps practices in data engineering workflows.