Senior DataBricks Engineer (Remote)
Design, build, and optimize scalable data pipelines and analytics solutions on the Databricks platform. Collaborate with stakeholders to deliver production-ready data products while ensuring data quality, security, and performance. Requires advanced expertise in Databricks, Spark, and cloud platforms with Python/Scala proficiency.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
Any VISA's are welcome.
We can do H1B transfer and GC as well.
Must be open to Relocate to one of these states NJ, NC, CT
IMMEDIATE REQUIREMTMENT
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Company Description Silicon Tech Solutions, Inc. is a global IT solutions and services provider founded in 2010 in California, USA. The organization specializes in emerging and innovative technologies that help businesses across major industries gain a competitive edge in the digital era.
Role Description The Sr DataBricks Engineer is a full-time remote role responsible for designing, building, and optimizing data pipelines and analytics solutions on the Databricks platform. This role involves developing scalable ETL processes, integrating data from multiple sources, and implementing best practices for data quality, security, and performance. The Sr DataBricks Engineer collaborates closely with data architects, data analysts, and business stakeholders to translate requirements into reliable, production-ready data products. Day-to-day tasks include writing and maintaining code in languages such as Python or Scala, managing Spark clusters, monitoring workloads, troubleshooting issues, and supporting continuous improvement of the data engineering environment. The role also contributes to internal standards, documentation, and mentoring of junior engineers on Databricks and modern data engineering practices.
Qualifications
- Strong data engineering skills, including experience building scalable ETL/ELT pipelines, data warehousing, and working with large, complex datasets.
- Advanced experience with Databricks and Apache Spark, including cluster configuration, performance tuning, and workload optimization.
- Proficiency in programming languages commonly used in data engineering, such as Python and/or Scala, and solid understanding of SQL.
- Hands-on experience with cloud platforms (e.g., AWS, Azure, or GCP), data storage services, and related security and governance practices.
- Knowledge of modern data architectures and tools, such as Delta Lake, Lakehouse patterns, CI/CD pipelines, and version control (e.g., Git).
- Ability to work collaboratively in distributed teams, communicate clearly with technical and non-technical stakeholders, and document solutions effectively.
- Bachelor’s or master’s degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent practical experience.
- Experience in consulting or working on large enterprise projects, especially in areas like ERP, analytics, or cloud migrations, is beneficial.
- Familiarity with data modeling, data quality frameworks, and monitoring/observability tools is a plus.
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