Work with the Data Science teams to build datamarts and provide ad-hoc requests as needed

Develop and maintain data pipelines, ETL processes, and REST APIs for efficient data processing and delivery

Ensure the scalability, efficiency, and reliability of the data solutions

Provide on-call support to ensure the seamless operation of the data solutions

Collaborate with cross-functional teams to deliver high-quality data solutions in line with project goals and timelines

Continuously evaluate industry trends and best practices to refine and implement the most effective data engineering strategies

Guide and mentor junior team members, fostering a culture of growth and continuous learning within the group

Work directly with clients to understand their needs and deliver effective, tailored solutions

Collaborate with stakeholders, demonstrating excellent communication and leadership skills

Minimum of 3 years of experience in Data Software Engineering, working on large-scale data projects and complex data infrastructures

Proven experience in building and maintaining data pipelines, ETL processes, and REST APIs

Expertise in Amazon Web Services, specifically with data-related services such as Redshift, S3, and Glue

Solid experience with Apache Airflow and Apache Spark, using them for data processing and pipeline automation

Proficiency in Python and SQL for data processing purposes

Experience with Databricks and PySpark for pipeline automation

Experience with CI/CD tools for efficient delivery of data solutions

Strong analytical skills, enabling effective problem-solving and decision-making in complex data environments

Ability to effectively communicate technical ideas to a non-technical audience

Advanced English language skills (Upper-Intermediate level), allowing for effective written and spoken collaboration and participation in meetings and discussions with the team and stakeholders

Experience with Redshift for data warehousing and management