Middle Data Engineer (Healthcare domain)

Sigma Software is looking for a motivated Data Engineer to join our expanding engineering team.

If you want to work in a close-knit team of Data Engineers solving complex problems using advanced data collection, transformation, analysis, and monitoring, then this opportunity is for you.

We look forward to having you on our team!

Customer

Our client is a leading medical technology company. The portfolio of products, services, and solutions is at the center of clinical decision-making and treatment pathways. Patient-centered innovation has always been and will always be, at the core of the company. The client is committed to improving patient outcomes and experiences, regardless of where patients live or what they face. The client is innovating sustainably to provide healthcare for everyone, everywhere.

Project

The project’s mission is to enable healthcare providers to increase their value by equipping them with innovative technologies and services in diagnostic and therapeutic imaging, laboratory diagnostics, molecular medicine, and digital health and enterprise services.

Requirements

• Experience in data engineering and with cloud computing services solutions in the area of data and analytics, preferably with Azure

• Conceptual knowledge of data analysis fundamentals, e.g., dimensional modeling, ETL, reporting tools, data governance, data warehousing, structured and unstructured data

• Knowledge of SQL and experience with the Python programming language

• Excellent communication skills and fluency in business English

• Understanding Big Data databases, such as Snowflake, BigQuery, etc. Snowflake is preferred

• Experience with database development and data modeling, ideally with Databricks or Spark

Responsibilities

• Implement architecture based on Azure cloud platforms (Data Factory, Databricks, Event Hub)

• Design, develop, optimize, and maintain squad-specific data architecture and pipelines that adhere to defined ETL and Data Lake principles

• Discover, understand, and organize disparate data sources and structure them into clean data models with clear, understandable schemas

• Contribute to evaluating new tools for analytical data engineering or data science

• Suggest and contribute to training and improvement plans for analytical data engineering skills, standards, and processes