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. • 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. • Monitor, troubleshoot, and resolve pipeline failures and data quality issues, applying root-cause analysis to prevent recurrence. • Contribute to documentation of data pipelines, data dictionaries, and engineering standards. • Support the team in exploring and evaluating new tools and approaches to continuously improve the data infrastructure. 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. • 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. • 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. NICE TO HAVE • 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. • Experience with Delta Sharing or Lakehouse Federation concepts. 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 • ETL and ELT workflows using Spark SQL and PySpark • Incremental data processing and structured streaming • Production pipeline development and orchestration • Data governance and security within the Databricks environment