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 seeking an experienced Data Engineer to join our data team. As a Data Engineer, you will be responsible for designing, building, and maintaining data pipelines, data integration processes, and data infrastructure using Dataiku. You will collaborate closely with data scientists, analysts, and other stakeholders to ensure efficient data flow and support data-driven decision making across the organization.
- 8+ years of overall industry experience specifically in data engineering
- Strong knowledge of data engineering principles, data integration, and data warehousing concepts.
- Strong understanding of the pharmaceutical/ Life Science domain, including knowledge of patient data, Commercial data, drug development processes, and healthcare data.
- Proficiency in data engineering technologies and tools, such as SQL, Python, ETL frameworks, data integration platforms, and data warehousing solutions.
- Experience with data modeling, database design, and data architecture principles.
- Familiarity with big data technologies (e.g., Hadoop, Spark) and cloud platforms - AWS, Azure
- Strong analytical and problem-solving skills, with the ability to work with large and complex datasets.
- Strong communication and collaboration abilities.
- Attention to detail and a focus on delivering high-quality work.
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.
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