Join our team as a Data Engineer and play a crucial role in designing, developing, and maintaining robust data solutions. You will be instrumental in building data pipelines, ensuring data integrity, and implementing ETL processes to facilitate seamless data migration and deployment across various systems. This role offers a fantastic opportunity to work with cutting-edge technologies and contribute to impactful data initiatives.

We are seeking a professional who can architect and implement scalable data infrastructure, manage complex data flows, and ensure the reliability of our data services. Your expertise will be vital in transforming large datasets and enabling data-driven decision-making for our organization.

As a Data Engineer, you will lead the design and development of highly scalable data pipelines and ETL/ELT processes. You will be responsible for building and maintaining data services and APIs, ensuring seamless data consumption across different systems. Maintaining data quality, validation, and consistency will be a core part of your role, alongside optimizing data workflows for performance, scalability, and cost-efficiency.

You will handle high-volume and high-velocity datasets using efficient processing strategies. Additionally, you'll mentor junior team members, fostering their professional development and continuously improving data engineering processes to boost team productivity.

We require a candidate with a minimum of 2-5 years of professional experience, including at least 3 years specifically in Python. You must possess proficiency in Python programming and have strong experience building scalable data pipelines and distributed data processing systems.

A solid understanding of both SQL and NoSQL databases, along with advanced query optimization techniques, is essential. Experience with ETL/ELT frameworks, data integration patterns, and multi-cloud platforms (AWS, Azure, or Google Cloud) is required. Familiarity with workflow orchestration tools like Apache Airflow and CI/CD tools (Git, Jenkins) is also necessary. A comprehensive understanding of performance tuning, scalability, distributed system design principles, data quality, and governance frameworks is expected.