Data Engineer

Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best analytics global consulting team in the world.

We are looking for an experienced AWS Databricks Data Engineer to design, develop, integrate, and troubleshoot data solutions supporting cryptographic asset and risk management. The ideal candidate will have strong hands-on experience with AWS, Databricks, Python, PySpark, and advanced SQL, along with experience working with APIs, data modeling, data quality, and CI/CD.

Requirements

Strong hands-on experience with AWS and Databricks.

Strong programming experience in Python and PySpark.

Advanced proficiency in SQL, including complex queries, joins, transformations, and data analysis.

Experience working with REST APIs, JSON, and API-based data integration.

Experience with data modeling and schema mapping.

Hands-on experience with data quality, validation, reconciliation, and troubleshooting.

Experience with GitHub and CI/CD processes.

Experience using Visual Studio Code or a similar development environment.

Ability to understand, analyze, troubleshoot, and modify existing code.

Strong root-cause analysis and problem-solving skills.

Ability to work with multiple technical and business stakeholders in a complex enterprise environment.

Strong written and verbal communication skills.

Curiosity and willingness to learn cryptography concepts and datasets quickly.

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, challenging, and entrepreneurial environment, with a high degree of individual responsibility.