Description

What You'll Do: 

• Design, develop, and optimize complex SQL queries, stored procedures, CTEs, window functions, and database objects.
• Perform query tuning, execution plan analysis, indexing, and partitioning to improve database performance and scalability.
• Build and maintain scalable data pipelines for data ingestion, transformation, and analytics workloads.
• Design and implement modern data architectures covering ingestion, storage, processing, and compute layers.
• Work with cloud-based data warehouses including Snowflake, BigQuery, Redshift, and Azure SQL.
• Develop and manage cloud-native data solutions across AWS, Azure, and GCP platforms.
• Integrate data from multiple structured, semi-structured, and unstructured data sources.
• Develop and maintain ETL/ELT workflows using tools such as Airflow, Azure Data Factory (ADF), AWS Glue, Databricks, or similar platforms.
• Automate data processes to improve reliability, scalability, operational efficiency, and reduce manual intervention.
• Ensure data quality, governance, security, compliance, and data lineage standards are maintained across the data ecosystem.
• Collaborate with Data Analysts, Data Scientists, Product Managers, and Business Teams to understand business requirements.
• Translate business requirements into scalable and maintainable data engineering solutions.
• Monitor, troubleshoot, and optimize data pipelines, workflows, database performance, and platform health.
• Participate in architecture reviews and contribute to data platform modernization initiatives.
• Mentor junior data engineers and promote engineering best practices, coding standards, and technical excellence.
• Create technical documentation, architecture diagrams, operational runbooks, and deployment guides.

Expertise You'll Bring:

• 2-5 years of experience in Data Engineering, Data Warehousing, or related fields.
• Advanced expertise in SQL including complex query writing, stored procedures, CTEs,  window functions, and query optimization.
• Strong experience in database performance tuning, execution plan analysis, indexing  strategies, and partitioning.
• Hands'on experience with relational databases and cloud data warehouses such as Snowflake, BigQuery, Amazon Redshift, and Azure SQL.
• Experience designing and implementing scalable data architectures and modern data  platforms.
• Strong knowledge of AWS, Azure, and GCP cloud services for data engineering  workloads.
• Experience building and managing ETL/ELT pipelines and enterprise data integration  solutions.
• Hands'on experience with workflow orchestration tools such as Apache Airflow, Azure  Data Factory (ADF), AWS Glue, Databricks, or similar platforms.
• Experience integrating structured, semi-structured, and unstructured data sources.
• Strong understanding of data modeling, data warehousing concepts, and analytics  platforms.
• Proficiency in Python, SQL, or other data engineering programming languages.
• Knowledge of data governance, security, compliance, and data quality best practices.
• Experience working with large-scale data processing and analytics environments.
• Strong analytical, troubleshooting, and problem-solving skills.
• Excellent communication and stakeholder management skills.
• Experience mentoring junior engineers and leading technical best practices.
• Ability to collaborate effectively with business and technical stakeholders in Agile  delivery environments

Key Skills

Education

Any Graduate