Description

Job Description

Key Responsibilities

• Design, develop and maintain scalable data pipelines for batch and real-time data processing.
• Build and optimize ETL/ELT workflows for data ingestion, transformation and loading.
• Develop and manage data models, data lakes and data warehouses.
• Ensure data quality, data governance and data security standards are maintained.
• Collaborate with Data Analysts, Data Scientists, Business Teams and Application Developers.
• Monitor and troubleshoot data pipeline performance issues.
• Implement automation for data validation, monitoring and reporting.
• Optimize database performance and query execution.
• Support cloud migration and modernization initiatives.
• Participate in Agile/Scrum ceremonies and contribute to end-to-end delivery.
• Candidates should be comfortable using coding assistance tools.

Required Skills

• SQL and Advanced PL/SQL
• Python or PySpark
• ETL/ELT Development
• Data Warehousing Concepts
• Data Modeling
• Apache Spark
• Hadoop Ecosystem
• Azure Data Factory, AWS Glue, GCP Dataflow
• Airflow or Similar Workflow Orchestration Tools
• Git Version Control
• Linux/Unix

Good to Have

• Databricks
• Snowflake
• Kafka
• Delta Lake
• Azure Synapse Analytics
• BigQuery
• Redshift
• CI/CD for Data Pipelines
• Terraform or Infrastructure as Code

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering or related field.
• 5–8 years of experience in Data Engineering and Data Platform Development.
• Strong analytical, troubleshooting and problem-solving skills.
• Effective communication and stakeholder management abilities.

Key Competencies

• Data Pipeline Development
• Data Warehousing / Data Modeling
• ETL/ELT Engineering
• Big Data Processing
• Cloud Data Engineering
• Performance Optimization
• Data Governance / Security

Key Skills

Education

Any Graduate