Strong Middle Data Engineer
Design and develop scalable data platforms and pipelines, leveraging cloud technologies and distributed frameworks to support analytics and operational needs.
Do you enjoy building scalable data solutions that enable analytics, reporting, and business intelligence? We are looking for a Middle Data Engineer to join a remote team working on modern cloud-native data platforms and distributed data processing systems.
In this role, you will contribute to the development and optimization of ETL/ELT pipelines, data models, and cloud-based data architectures using technologies such as Databricks, Apache Spark, Snowflake, and AWS. This opportunity is ideal for engineers who want to deepen their expertise in modern Data Engineering practices while working on impactful business solutions.
We at Sigma Software value collaboration, continuous learning, and technical excellence. Joining our team means working in a supportive engineering environment with opportunities for professional growth, knowledge sharing, and long-term career development.
CUSTOMER
Our customer operates within a modern data-driven environment focused on analytics, reporting, and operational decision-making. The platform ecosystem combines cloud-native technologies, distributed processing frameworks, and scalable infrastructure to support reliable and high-performance data solutions.
PROJECT
The project focuses on building and maintaining scalable cloud-based data platforms that support analytics and business intelligence initiatives. The team develops ETL/ELT pipelines, optimizes data transformations, and enhances modern data architectures using distributed data processing technologies and cloud infrastructure.
Key Technologies: Python, SQL, Apache Spark, Databricks, Snowflake, AWS, ETL/ELT, CI/CD
WILL BE A PLUS
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PERSONAL PROFILE
Design and develop scalable data platforms and pipelines, leveraging cloud technologies and distributed frameworks to support analytics and operational needs.
Design and build scalable data platforms and pipelines to support analytics and data-driven decision-making, ensuring data quality and reliability across multiple projects.
Develop and optimize Python-based data pipelines using Apache Airflow, building DAGs and ETL/ELT workflows while integrating data sources and improving pipeline reliability, performance, and observability.
Design, build, and maintain scalable data warehouse and lake solutions, developing reliable data pipelines and improving data quality and performance to support analytics and business decisions.
Map, transform, and validate data from higher education systems to create a unified data model, with traceable specifications and quality checks that support reliable analysis of student outcomes.