About the Role

We are looking for an experienced Senior Data Engineer to design, build, and optimize scalable data platforms and pipelines. The ideal candidate will have strong expertise in modern data engineering technologies, cloud-based lakehouse architectures, and enterprise data modeling. You will work closely with data architects, analysts, and business stakeholders to deliver high-quality, reliable, and scalable data solutions.

Key Responsibilities

Design, develop, and maintain scalable ETL/ELT pipelines using PySpark and Databricks.
Build and optimize enterprise-grade data solutions using the Lakehouse/Medallion Architecture.
Develop high-performance data transformations and optimize complex SQL queries.
Design and implement Data Vault 2.0 models for enterprise data warehousing.
Integrate data from multiple structured and unstructured sources.
Ensure data quality, governance, security, and performance across the data platform.
Collaborate with architects, data scientists, analysts, and business teams to understand data requirements.
Monitor, troubleshoot, and optimize data pipelines for reliability and scalability.
Follow engineering best practices, including code reviews, documentation, and CI/CD processes.
Mentor junior engineers and contribute to technical design discussions.

Required Skills

9–12 years of experience in Data Engineering.
Strong hands-on experience with Databricks and PySpark.
Advanced proficiency in SQL, including performance tuning and query optimization.
Deep understanding of Lakehouse Architecture and the Medallion (Bronze, Silver, Gold) framework.
Experience implementing Data Vault 2.0 data modeling.
Strong knowledge of ETL/ELT processes and data integration.
Experience working with large-scale data processing and distributed computing.
Familiarity with data governance and data quality best practices.
Excellent problem-solving, communication, and stakeholder management skills.

About the Role

Key Responsibilities

Design, develop, and maintain scalable ETL/ELT pipelines using PySpark and Databricks.

Build and optimize enterprise-grade data solutions using the Lakehouse/Medallion Architecture.

Develop high-performance data transformations and optimize complex SQL queries.

Design and implement Data Vault 2.0 models for enterprise data warehousing.

Integrate data from multiple structured and unstructured sources.

Ensure data quality, governance, security, and performance across the data platform.

Collaborate with architects, data scientists, analysts, and business teams to understand data requirements.

Monitor, troubleshoot, and optimize data pipelines for reliability and scalability.

Follow engineering best practices, including code reviews, documentation, and CI/CD processes.

Mentor junior engineers and contribute to technical design discussions.

Required Skills

9–12 years of experience in Data Engineering.

Strong hands-on experience with Databricks and PySpark.

Advanced proficiency in SQL, including performance tuning and query optimization.

Deep understanding of Lakehouse Architecture and the Medallion (Bronze, Silver, Gold) framework.

Experience implementing Data Vault 2.0 data modeling.

Strong knowledge of ETL/ELT processes and data integration.

Experience working with large-scale data processing and distributed computing.

Familiarity with data governance and data quality best practices.

Excellent problem-solving, communication, and stakeholder management skills.