▷ Only 24h Left: Data Engineer: Data Bricks Practice Lead
19 hours ago
Responsibilities:
- Lead and grow the Databricks Data Engineering practice, developing service offerings, methodologies, and best practices tailored to customer needs.- Oversee multiple customer projects, providing technical leadership and ensuring high-quality delivery of Databricks-based data engineering solutions.- Architect complex data solutions leveraging Databricks technologies, including Delta Lake, Apache Spark, and MLflow, addressing customers' data processing, analytics, and machine learning needs.- Collaborate with sales teams to develop proposals, scope projects, and articulate the value proposition of Databricks solutions to potential clients.- Mentor and guide data engineers within the practice, fostering their professional growth and enhancing the team's overall Databricks expertise.- Stay at the forefront of Databricks technologies, features, and best practices, ensuring our practice remains cutting-edge and competitive.- Develop and maintain strong relationships with Databricks, participating in partner programs and leveraging resources to enhance our service capabilities.- Create training programs to upskill internal teams on Databricks technologies and best practices.- Work closely with other practice leads to identify cross-practice opportunities and deliver integrated solutions to customers.
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Qualifications:
- Bachelors or masters degree in computer science, Data Engineering, or a related technical field.- 10+ years of experience in data engineering, with at least 3-5 years of hands-on experience working with Databricks technologies.- Proven expertise in designing and implementing large-scale data processing solutions using Databricks, Delta Lake, and Apache Spark.- Strong understanding of cloud platforms (AWS, Azure, GCP) and their integration with Databricks.- Proficiency in languages commonly used in Databricks environments, such as Python, Scala, and SQL.- Experience with data lakehouse architectures, ETL/ELT processes, and data modeling techniques.- Familiarity with machine learning workflows and MLOps practices, particularly using MLflow.- Proven track record of leading successful customer engagements and managing complex data engineering projects.- Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.- Strong leadership and mentoring abilities, with experience in building and growing technical teams.- Databricks certifications (e.g., Databricks Certified Associate and/or Professional) are highly desirable.- Experience with Agile methodologies and their application in data engineering projects.- Understanding of data governance, security, and compliance requirements in various industries.- Ability to travel as needed to client sites and Databricks events.
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