Middle Data Engineer Healthcare domain

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

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

We look forward to having you on our team!

Customer

Our client is a leading medical technology company. Its 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 Customer is innovating sustainably to provide healthcare for everyone, everywhere.

Project

The project’s mission is to increase the value of healthcare providers by equipping them with innovative technologies and services for diagnostic and therapeutic imaging, laboratory diagnostics, molecular medicine, 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 of big data databases, such as Snowflake and BigQuery. Snowflake is preferred

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

Responsibilities

Implement an 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, structuring them into clean data models with clear, understandable schemas

Contribute to the evaluation of 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