Remote Senior Data Engineer at Proxify AB

Senior Data Engineer

Posted 15 days ago

About us:

Talent has no borders. Proxify's mission is to connect top developers around the world with the opportunities they deserve. So, it doesn't matter where you are; we are here to help you fast-track your independent career in the right direction. 🙂

Since our launch, Proxify's developers have successfully worked with 1200+ happy clients to build their products and growth features. 5000+ talented developers trust Proxify and its network to fulfill their dreams and objectives.

Proxify is shaped by a global network of supportive, talented developers interested in remote full-time jobs. Our Glassdoor (4.5/5) and Trustpilot (4.8/5) ratings reflect the trust developers place in us and our commitment to our members' success.

Apply now

The Role:

We are looking for a Senior Data Engineer for one of our clients. You are a perfect candidate if you are growth-oriented, you love what you do, and you enjoy working on new ideas to develop exciting products.

What we’re looking for:

Minimum of 5 years of professional experience in Data Engineering.
Proven experience designing and implementing data architectures and ETL processes.
Proficiency in data engineering tools and languages (e.g., Python, SQL, Apache Spark).
Experience with data warehouse platforms (e.g., Amazon Redshift, Google BigQuery, Snowflake).
Familiarity with cloud platforms (AWS, GCP, or Azure).
Strong understanding of data modelling concepts and the ability to design effective data models.
Data Governance: Knowledge of data governance principles, data quality management, and data security best practices.

Must-have skills:

SQL
Python / Java
ETL

Responsibilities:

Design, implement, and maintain scalable and efficient data architectures, including data warehouses, data lakes, and ETL processes.
Lead the development of robust and scalable Extract, Transform, Load (ETL) processes to ensure the seamless flow of data between systems.
Collaborate with cross-functional teams to integrate data from various sources, ensuring data quality, consistency, and accuracy.
Develop and implement data models to support business requirements, ensuring data is structured for optimal performance and analytical capabilities.
Identify and address performance bottlenecks within the data infrastructure, optimising queries and processes for efficiency.
Implement and enforce data governance policies, ensuring compliance with data privacy regulations and maintaining data security.
Implement automation strategies for data processing, monitoring, and maintenance tasks, reducing manual intervention and improving reliability.