Data Engineering Leader for High-Performance Data Stack
5 days ago
SeenThis is seeking a seasoned Data Engineering Leader to spearhead the maintenance and evolution of our high-performance data stack. You will be at the forefront of implementing best practices and driving innovation in our data architecture.
The ideal candidate will have experience working with big data technologies and high-volume, real-time data processing. They will also have a strong understanding of data modeling, ETL processes, and data warehousing concepts.
We're looking for someone who can balance maintenance of existing systems with innovation and improvement. As a Data Engineering Leader, you will be responsible for collaborating across various departments, handling commercial, financial, and product data to create a holistic and inclusive data environment.
Key responsibilities include maintaining, optimizing, and scaling our existing data pipelines to meet evolving business needs. The successful candidate will also take ownership of diverse data projects, including AI/ML implementations, and collaborate effectively with stakeholders to ensure alignment between data solutions and business objectives.
We believe the ideal candidate will have relevant education, 5+ years of experience as an ML Engineer, Data Scientist, Data Engineer, or similar positions, and extensive experience with Python and SQL in data engineering contexts.
The estimated salary for this role is around $180,000 per year, considering the company's location and industry standards. However, we offer competitive benefits and opportunities for growth and professional development.
- Relevant education with a degree in engineering or similar
- 5+ years of experience as an ML Engineer, Data Scientist, Data Engineer, or similar positions
- Experience in modern tools for data collection and aggregation
- Extensive experience with Python and SQL in data engineering contexts
- Good understanding of Kubernetes and Docker
- Proven track record working with big data technologies and high-volume, real-time data processing
- Strong understanding of data modeling, ETL processes, and data warehousing concepts
- Ability to balance maintenance of existing systems with innovation and improvement
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