Allata is a global consulting and technology services firm founded in 2016, with 350+ employees across the US, India, and Argentina. We partner with some of the world's largest enterprises to move AI from idea to production — building intelligent agents, data foundations, and governance frameworks that scale.
Our teams work at the intersection of strategy, engineering, data, and design, helping clients modernize their technology, unlock data value, and create meaningful digital experiences. At Allata, you'll join an agile, cross-functional team that works closely alongside clients — making a real impact and building lasting partnerships.
We are seeking a skilled Data Engineer to join our team and contribute to data-driven initiatives within the healthcare industry. This role focuses on designing, building, and optimizing scalable data solutions that support analytics, reporting, and advanced data use cases in regulated environments.
Role & Responsibilities:
Design, develop, and maintain scalable data pipelines using Databricks 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
Hard Skills - Must have:
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 Pysparkbased 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, Auroraor a comparable engine.
Strong experience with batch and streaming data processing techniques and file compactization strategies.
Hard Skills - Nice to have/It's a plus:
Strong hands-on experience with Databricks in Azure environments.
Advanced proficiency in Python and PySpark for distributed data processing.
Experience building and optimizing data pipelines in Azure (Azure Data Factory, Azure SQL, Data Lake Storage, etc.).
Solid understanding of data warehousing, data lakehouse concepts, and ETL/ELT frameworks.
Experience working with relational databases such as SQL Server, PostgreSQL, Oracle, or similar.
Knowledge of batch and streaming data processing patterns.
Experience working with large, complex datasets in cloud-based distributed environments.
Soft Skills / Business Specific Skills:
Strong analytical and problem-solving skills.
Ability to work effectively in cross-functional and distributed teams.
Clear communication skills, with the ability to explain technical concepts to non-technical stakeholders.
Proactive mindset with a strong sense of ownership.
Commitment to delivering high-quality, reliable data solutions.
