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Data Lead Engineer

Laiba Technologies LLCFremont, CA🇺🇸United StatesPosted Oct 9, 2026

Quick Overview

Seniority
Mid Senior
Work mode
On Site
Location
Fremont, CA, United States
Posted
19 hours ago

Job Description

Title: Data Engineer (Lead/Sr.)
Location: Fremont, CA (Onsite)
Core Skills: Databricks, Apache Spark, PySpark, SQL, Spark SQL, ETL, ELT, API Integration, Data Warehousing, Schema Design, Dimensional Modeling, Medallion Architecture.
Lead Data Engineer with strong expertise in Databricks, Apache Spark, PySpark, SQL, and ETL/ELT development. The ideal candidate will lead the design and development of scalable data pipelines, work with structured and unstructured data, and implement robust data warehouse and lakehouse solutions using dimensional modeling and medallion architecture.
Required Qualifications
  • 10+ years of proven experience in data engineering, including ETL/ELT development.
  • Strong hands-on experience with Databricks and Apache Spark.
  • Excellent programming skills in PySpark, SQL, and Spark SQL.
  • Experience processing structured and unstructured data and integrating API-based data sources.
  • Strong understanding of schema design and dimensional data modeling.
  • Practical experience implementing medallion architecture.
  • Solid knowledge of data warehouse best practices, development standards, and methodologies.
  • Strong analytical, troubleshooting, and problem-solving skills.
  • Ability to work independently, learn new technologies, and take ownership of deliverables in a fast-paced, dynamic environment.
  • Strong communication and collaboration skills.
Key Responsibilities
  • Lead the design, development, and optimization of ETL/ELT pipelines for structured and unstructured data from multiple sources, including APIs.
  • Build and maintain scalable data processing solutions using Databricks, Apache Spark, PySpark, and Spark SQL.
  • Design database schemas and dimensional data models to support analytics and reporting.
  • Implement medallion architecture across Bronze, Silver, and Gold data layers.
  • Optimize SQL queries and Spark workloads for performance, reliability, and scalability.
  • Apply data warehouse best practices, development standards, and methodologies.
  • Collaborate with business stakeholders and technical teams to translate requirements into effective data solutions.
  • Provide technical guidance, review code, and troubleshoot data pipeline issues.
  • Maintain technical documentation and ensure data quality throughout the processing lifecycle.

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