Design, develop, and maintain scalable data pipelines using Databricks (PySpark) and Python.
Build and optimize ETL/ELT processes within Azure cloud environments.
Implement data models following modern Data Lakehouse principles (e.g., Medallion architecture).
Ensure data quality, consistency, and performance across ingestion, staging, and curated layers.
Collaborate with data architects, analysts, and business stakeholders to translate healthcare data requirements into technical solutions.
Develop reusable data transformation logic and modular processing components.
Support deployment processes following CI/CD and DevOps best practices.
Monitor and optimize data workflows for performance, scalability, and reliability.
Contribute to data governance, security, and compliance practices relevant to healthcare environments.

Current knowledge of an using modern data tools like (Databricks,FiveTran, Data Fabric and others)
Core experience with data architecture, data integrations, data warehousing, and ETL/ELT processes
Applied experience with developing and deploying custom whl and or in session notebook scripts for custom execution across parallel executor and worker nodes
Applied experience in SQL, Stored Procedures, and Pyspark based on area of data platform specialization.
Strong knowledge of cloud and hybrid relational database systems, such as MS SQL Server, PostgresSQL, Oracle, Azure SQL, AWS RDS, Aurora or a comparable engine.
Strong experience with batch and streaming data processing techniques and file compactization strategies.

Allata is an equal opportunity employer.
We celebrate diversity and are committed to creating an inclusive environment for all employees.
This policy applies to all terms and conditions of employment, including but not limited to, recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

Data Engineer, Databricks, Python, Azure

Key skills