Data Engineer

Tata Power; Bhubaneshwar; On-site; INR 600,000 - 900,000; Full time

Job summary

Tata Power in Bhubaneshwar is seeking an enthusiastic Data Engineer with 1–3 years of hands-on experience in data engineering, ETL/ELT pipelines, data warehousing, and cloud data platforms.

You will build scalable data pipelines, work with structured and unstructured data, and support analytics and reporting using Azure Data Factory, Databricks, AWS, and Azure. Strong SQL, Python/PySpark, and knowledge of data modelling are essential.

Qualifications

• Bachelor's or Master's in Computer Science or Information Technology.
• 1 to 3 years of experience in data engineering, ETL development, data pipelines, or data platform projects.
• Hands-on experience in building and maintaining ETL/ELT pipelines.
• Experience with cloud platforms such as AWS or Azure.

Responsibilities

• Build and maintain ETL/ELT pipelines.
• Support analytics and reporting platforms with scalable data workflows.
• Work with structured and unstructured data to support analytics.
• Develop and optimize data pipelines and data warehouse models.

Skills

SQL proficiency

Python/PySpark

ETL/ELT pipelines

Data warehousing

Data modelling

Batch & streaming processing

EDA & data profiling

Data quality & validation

Education

Bachelor's or Master's in CS/IT

Tools

Azure Data Factory

AWS Glue

Databricks

Azure Data Lake

AWS S3

Azure Synapse

Redshift

Snowflake

Job description

We are looking for an enthusiastic and highly motivated Data Engineer with 1 to 3 years of experience and a strong foundation in data engineering, ETL/ELT pipelines, data warehousing, and cloud data platforms. The ideal candidate should have hands-on experience in building data pipelines, working with structured and unstructured data, and supporting analytics and reporting platforms using tools such as Azure Data Factory, Databricks, AWS, and Azure. This role is ideal for candidates who are passionate about building scalable data pipelines, working with large datasets, and developing modern data platforms in cloud environments.

Requirements:

• Experience: 1-8 years.
• Education: Bachelor's/Master's in Computer Science or Information Technology.
• Bachelor's or master's degree in computer science or information technology.
• 1 to 3 years of experience in data engineering, ETL development, data pipelines, or data platform projects.
• Hands-on experience in building and maintaining ETL/ELT pipelines.
• Experience working with cloud platforms such as AWS or Azure is preferred.

Technical Expertise:

• Strong knowledge of SQL and database concepts.
• Proficiency in Python or PySpark for data processing.
• Experience in building ETL/ELT pipelines and data workflows.
• Understanding of data warehousing concepts and data modelling (Star Schema, Snowflake Schema).
• Experience working with structured and semi-structured data (CSV, JSON, Parquet, etc. ).
• Understanding of data lakes, data warehouses, and lakehouse architecture.
• Knowledge of data quality, data validation, and data reconciliation techniques.
• Understanding of batch and streaming data processing concepts.
• Experience performing Exploratory Data Analysis (EDA) and data profiling.

Frameworks and Tools:

• Experience with Azure Data Factory or AWS Glue for data integration and pipeline development.
• Hands-on experience with Databricks and PySpark for data transformation and processing.
• Experience with Azure Data Lake / AWS S3 storage.
• Familiarity with data warehouses such as Azure Synapse, Redshift, Snowflake, or Databricks SQL.
• Understanding of workflow orchestration tools and scheduling pipelines.
• Experience in data ingestion from APIs, databases, flat files, and streaming sources.
• Basic understanding of CI/CD for data pipelines is an added advantage.

Additional Knowledge:

• Understanding of big data technologies such as Hadoop and Spark.
• Familiarity with streaming technologies such as Kafka or Azure Event Hub is a plus.
• Knowledge of data governance, metadata management, and data security concepts is an advantage.
• Exposure to performance tuning and optimization of data pipelines is preferred.

Preferred Qualification:

• Participation in data engineering projects, hackathons, or technical competitions.
• Contributions to GitHub projects, technical blogs, or technical documentation are an added advantage.
• Relevant certifications in Azure, AWS, Databricks, or data engineering are a plus.