Managing Consultant Data Engineering

The ideal candidate will be responsible for designing, building, and managing data infrastructure and pipelines that support analytics and machine learning initiatives. This role involves close collaboration with data scientists, analysts, and cross-functional teams to ensure data integrity, optimize processing workflows, and develop robust data solutions that align with business objectives. The successful candidate will play a vital role in enabling data-driven decision-making by creating scalable and efficient data systems.

Key Responsibilities
Design, develop, and optimize data pipelines and workflows to efficiently gather, clean, and process large datasets. Build scalable and reliable data models that support analytics, reporting, and decision-making processes. Develop and maintain ETL (Extract, Transform, Load) processes to collect, store, and retrieve data effectively. Collaborate with data scientists and analysts to define and implement data requirements, ensuring alignment with business goals. Implement data quality checks and monitoring mechanisms to uphold high data integrity and accuracy. Maintain and optimize data infrastructure to guarantee high availability, performance, and scalability. Integrate data from diverse sources and manage data storage solutions across both cloud and on-premise environments. Document data architecture, pipeline designs, and ETL processes to promote transparency and facilitate knowledge sharing.

Required Qualifications
Bachelor’s degree in Computer Science, Data Science, Engineering, or a related technical field. Proven experience as a Data Engineer or in a similar role involving data pipeline development and management. Proficiency in SQL and Python programming languages, along with hands-on experience using ETL tools. Experience working with various data storage systems, including SQL databases, data lakes, and data warehouses. Strong knowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP). Familiarity with big data technologies like Hadoop and Apache Spark. Solid understanding of data modeling, data architecture principles, and data warehousing concepts.

This position offers an exciting opportunity to contribute to a dynamic environment where data-driven decision-making is a priority. The role is critical in supporting advanced analytics and machine learning capabilities through effective data engineering practices, providing a platform for professional growth and impactful work.