Data Engineer with Azure

We are STX Next, a global IT consulting company specializing in customer-focused software services. Join a group of 500 professionals dedicated to helping customers build outstanding products. Leveraging the latest advancements in the field, and a passion for innovation, we're shaping the future of technology one project at a time.

Technical expertise:

• Proficiency in Microsoft Azure and its data solutions.
• Solid experience in designing scalable data processing systems (including pipelines, advanced ETL processes, data warehouses, and lakes).
• Strong SQL skills and experience with relational databases (MySQL, PostgreSQL).
• Familiarity with NoSQL databases (e.g., HBase, DynamoDB, MongoDB).
• Familiarity with data warehousing solutions (e.g., Snowflake, Databricks, BigQuery, Redshift).
• Understanding of distributed data processing systems (e.g., Apache Spark, Apache Flink).

Software development practices:

• Proficiency in Python 3.x or Scala, with knowledge of advanced Python constructs (e.g., lambda functions, generators, list comprehensions).
• Experience with threading and multi-process computation.
• Familiarity with Git for version control and Docker for containerization.

Soft skills:

• Good communication skills in English (minimum B2).
• A passion for continuous learning and embracing new technologies.
• Strong problem-solving skills and analytical thinking.

Extra points for:

• Experience with CI/CD tools (e.g., GitHub Actions).
• Hands-on experience with big data frameworks (e.g., Hadoop, Hive, Presto).
• Development skills in REST API or GraphQL.

• Develop innovative solutions for complex data challenges, using creativity and technology.
• Design and build data systems that fulfill client requirements and optimize user experience.
• Collaborate across teams to integrate unique approaches and out-of-the-box thinking into problem-solving.
• Serve as a trusted consultant for customized data solutions for global clients.
• Handle large-scale data processing and manage distributed systems to ensure reliability and performance.
• Uphold high data quality standards and refine approaches to align with best practices.
• Foster a culture of collaboration, craftsmanship, and continuous improvement