Azure Data Engineer

We are looking for a Senior Data Engineer with strong expertise in SQL, Python, Azure Synapse, Azure Data Factory, Snowflake, and Databricks. The ideal candidate should have a solid understanding of SQL (DDL, DML, query optimization) and ETL pipelines while demonstrating a learning mindset to adapt to evolving technologies.

Key Responsibilities :

- Collaborate with business and IT stakeholders to define business and functional requirements for data solutions.

- Design and implement scalable ETL/ELT pipelines using Azure Data Factory, Databricks, and Snowflake.

- Develop detailed technical designs, data flow diagrams, and future-state data architecture.

- Evangelize modern data modelling practices, including entity-relationship models, star schema, and Kimball methodology.

- Ensure data governance, quality, and validation by working closely with quality engineering teams.

- Write, optimize, and troubleshoot complex SQL queries, including DDL, DML, and performance tuning.

- Work with Azure Synapse, Azure Data Lake, and Snowflake for large-scale data processing.

- Implement DevOps and CI/CD best practices for automated data pipeline deployments.

- Support real-time streaming data processing with Spark, Kafka, or similar technologies.

- Provide technical mentorship and guide team members on best practices in SQL, ETL, and cloud data solutions.

- Stay up to date with emerging cloud and data engineering technologies and demonstrate a continuous learning mindset.

Required Skills & Qualifications :

Primary Requirements :

- SQL Expertise Strong hands-on experience with DDL, DML, query optimization, and performance tuning.

- Programming Languages Proficiency in Python or Java for data processing and automation.

- Data Modelling Good understanding of entity-relationship modelling, star schema, and Kimball methodology.

- Cloud Data Engineering Hands-on experience with Azure Synapse, Azure Data Factory, Azure Data Lake, Databricks and Snowflake

- ETL Development Experience building scalable ETL/ELT pipelines and data ingestion workflows.

- Ability to learn and apply Snowflake concepts as needed.

- Communication Skills : Strong presentation and communication skills to engage both technical and business stakeholders in strategic discussions.

- Financial Services Domain (Optional) : Knowledge of financial services.

Good to Have Skills :

- DevOps & CI/CD Experience with Git, Jenkins, Docker, and automated deployments.

- Streaming Data Processing Experience with Spark, Kafka, or real-time event-driven architectures.

- Data Governance & Security Understanding of data security, compliance, and governance frameworks.

- Experience in AWS Knowledge of AWS cloud data solutions (Glue, Redshift, Athena, etc.) is a plus.

Primary Requirements :

- SQL Expertise : Strong hands-on experience with DDL, DML, query optimization, and performance tuning.

- Programming Languages : Proficiency in Python or Java for data processing and automation.

- Data Modelling : Good understanding of entity-relationship modelling, star schema, and Kimball methodology.

- Cloud Data Engineering : Hands-on experience with Azure Synapse, Azure Data Factory, Azure Data Lake, Databricks and Snowflake

- ETL Development : Experience building scalable ETL/ELT pipelines and data ingestion workflows.

- Ability to learn and apply Snowflake concepts as needed.

- Communication Skills : Strong presentation and communication skills to engage both technical and business stakeholders in strategic discussions

Notice period : 30days