A successful Data Engineer will play a critical role in designing, building, and maintaining our data infrastructure. You will be responsible for developing and optimizing data pipelines, ensuring data quality and integrity, and supporting data-driven decision-making across the organization.
This job requires working in afternoon hours starting from 2PM CET or later.
Client Company’s mission is ensuring the smooth functioning of the internet. It oversees the critical infrastructure that underpins the interconnected global network, enabling a unified internet experience for all. This oversight encompasses three key areas of unique identifiers.
Maxima Consulting is an IT consulting company founded in 1993 in Boston. Our technology experts across North America, Europe, Asia, and Australia help organizations of all shapes and sizes in their digital transformation efforts. We provide effective and dependable solutions to the IT infrastructure, software development, quality assurance, maintenance & support, and cybersecurity challenges, as well as a broad scope of additional services.
responsibilities :
Design, develop, and maintain data pipelines for ingesting, transforming, and loading data from various sources.
Develop and maintain data warehouses and data lakes.
Design, implement, and optimize database schemas for PostgreSQL and MariaDB.
Develop and maintain data quality checks and data validation processes.
Collaborate with data analysts and data scientists to understand their data needs and provide appropriate solutions.
Develop and maintain data APIs for data consumption by other applications.
Participate in all phases of the data lifecycle, from data collection to data analysis and visualization.
Stay abreast of the latest data technologies and best practices.
Contribute to the continuous improvement of our data infrastructure and processes.
requirements-expected :
6+ years of experience as a Data Engineer or in a similar role.
Strong experience with data warehousing and data lake technologies.
Proficiency in SQL and experience with databases such as PostgreSQL and MariaDB.
Experience with data processing frameworks (e.g., Spark, Hadoop).
Experience with data pipelines and ETL/ELT processes.
Experience with scripting languages like Python.
Experience with data visualization tools (e.g., Metabase, Tableau, R).
Experience with data governance and data quality best practices.
