L1 Data Engineer - Remote

• Build and optimize data ingestion processes using Azure Data Factory and Databricks, ensuring data quality and consistency across all layers of the data platform. • Transform and process large datasets using PySpark and Python, applying best practices for performance and maintainability. • Write and optimize complex SQL queries to support analytical reporting and data validation requirements.

- 3+ years of professional experience in a Data Engineering or closely related role. - Strong proficiency in Python for data processing, transformation, and automation tasks. - Hands-on experience with Pandas for data manipulation and PySpark for distributed data processing.

• Transform and process large datasets using PySpark and Python, applying best practices for performance and maintainability. • Write and optimize complex SQL queries to support analytical reporting and data validation requirements. • Collaborate with data architects and senior engineers to implement and maintain data models aligned with organizational standards.

- Working knowledge of Azure Synapse Analytics, particularly Spark pool integration. - Solid SQL skills, including query writing, optimization, and performance tuning. - Familiarity with data engineering principles including incremental loading, data lake architecture, and Delta Lake.

NICE TO HAVE - Experience with SQL Server migration projects, including schema conversion and data movement. - Exposure to Terraform for Azure infrastructure provisioning and management.

- Databricks Lakehouse Platform architecture and capabilities - ETL and ELT workflows using Spark SQL and PySpark - Incremental data processing and structured streaming

We are looking for a motivated and technically solid L1 Data Engineer to join our growing Data & Analytics team. In this role, you will be responsible for designing, building, and maintaining the data architecture and infrastructure that supports our organization's data strategy. You will work hands-on to develop, test, and deploy reliable data solutions — ensuring pipelines are scalable, efficient, and aligned with business requirements. This is an ideal opportunity for a data professional who is eager to deepen their expertise in cloud-native data platforms, particularly within the Microsoft Azure and Databricks ecosystem, and who thrives in a collaborative, fast-paced environment. KEY RESPONSIBILITIES

• Design, develop, and maintain scalable data pipelines and ETL/ELT workflows to support business intelligence and analytics use cases. • Build and optimize data ingestion processes using Azure Data Factory and Databricks, ensuring data quality and consistency across all layers of the data platform. • Transform and process large datasets using PySpark and Python, applying best practices for performance and maintainability.

- Practical experience with Databricks, including notebook development, clusters, and job orchestration. - Experience building and managing data pipelines with Azure Data Factory. - Working knowledge of Azure Synapse Analytics, particularly Spark pool integration.

- Experience building and managing data pipelines with Azure Data Factory. - Working knowledge of Azure Synapse Analytics, particularly Spark pool integration. - Solid SQL skills, including query writing, optimization, and performance tuning.

- Experience with SQL Server migration projects, including schema conversion and data movement. - Exposure to Terraform for Azure infrastructure provisioning and management. - Familiarity with CI/CD practices applied to data engineering workflows.

- Hands-on experience with Pandas for data manipulation and PySpark for distributed data processing. - Practical experience with Databricks, including notebook development, clusters, and job orchestration. - Experience building and managing data pipelines with Azure Data Factory.

CERTIFICATION REQUIREMENT - Candidates are expected to hold or be actively working toward the Databricks Certified Data Engineer Associate certification. This certification validates foundational knowledge across the following domains: - Databricks Lakehouse Platform architecture and capabilities

- Candidates are expected to hold or be actively working toward the Databricks Certified Data Engineer Associate certification. This certification validates foundational knowledge across the following domains: - Databricks Lakehouse Platform architecture and capabilities - ETL and ELT workflows using Spark SQL and PySpark

- Production pipeline development and orchestration - Data governance and security within the Databricks environment

- Solid SQL skills, including query writing, optimization, and performance tuning. - Familiarity with data engineering principles including incremental loading, data lake architecture, and Delta Lake. - Understanding of data governance and security concepts within a cloud data platform.

- Familiarity with CI/CD practices applied to data engineering workflows. - Experience with Delta Sharing or Lakehouse Federation concepts. CERTIFICATION REQUIREMENT

- Strong proficiency in Python for data processing, transformation, and automation tasks. - Hands-on experience with Pandas for data manipulation and PySpark for distributed data processing. - Practical experience with Databricks, including notebook development, clusters, and job orchestration.