Lead the design and development of cloud-native data platforms and pipelines for analytics and data science. Own architecture decisions across data lake, lakehouse, and pipeline design. Serve as a technical leader and mentor, guiding engineers through best practices and complex problem solving.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
Data Engineering Lead / Principal Data Engineer
$150k annually
Fully Remote
This is a senior, hands-on technical leadership role focused on designing and building modern cloud data platforms. You will work closely with engineering, product, and business teams to deliver scalable data solutions that support analytics, reporting, and advanced data initiatives. This role is ideal for someone who enjoys owning architecture and implementation while guiding engineers and setting technical direction.
You will play a key role in shaping the evolution of a cloud-based data platform, with a strong focus on Databricks, lakehouse architecture, and scalable data pipelines. This is a highly visible role where you will influence best practices, mentor team members, and remain deeply involved in technical execution.
Responsibilities
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- Lead the design and development of cloud-native data platforms and pipelines for analytics and data science
- Own architecture decisions across data lake, lakehouse, and pipeline design
- Serve as a technical leader and mentor, guiding engineers through best practices and complex problem solving
- Contribute hands on to Databricks development including notebooks, workflows, and optimization efforts
- Establish standards for data modeling, ELT pipelines, orchestration, and performance tuning
- Ensure data platforms are reliable, secure, and scalable
- Partner with stakeholders to align technical solutions with business needs
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Technical Experience
- Strong experience with Azure data platforms including Azure Databricks, Data Lake, Fabric, and Data Factory
- Deep hands on expertise in Databricks and distributed data processing frameworks
- Proficiency in Python, PySpark, and SQL
- Experience designing modern data architectures such as lakehouse, streaming pipelines, and data mesh
- Strong understanding of data governance, security, and performance optimization
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