Data

Data Engineering Manager

San Jose
Work Type: Contract
Job Overview
As a Data Engineering Manager, you will be responsible for leading a team of data engineers to design, develop, and maintain our data infrastructure. You will work closely with cross-functional teams, including data science, analytics, and product teams, to ensure seamless data integration and accessibility.

Key Responsibilities
  • Lead, mentor, and grow a team of data engineers, fostering a culture of collaboration and innovation.
  • Collaborate with data science, analytics, and software engineering teams to optimize data workflows.
  • Drive the adoption of best practices in data engineering, including performance optimization, automation, and documentation.
  • Stay up to date with emerging technologies and trends in data engineering and big data.
  • Architect and maintain microservices-based data platforms to support high-availability and real-time data processing.
  • Design and optimize NoSQL databases to handle large-scale structured and unstructured data.
  • Manage stakeholder expectations and translate business requirements into technical solutions.
  • Monitor and improve data processing performance, reliability, and cost efficiency.
Required Qualifications
  • 8+ years of experience in data engineering, with at least 3+ years in a leadership or management role.
  • Excellent problem-solving and team leadership skills.
  • Ability to communicate technical concepts effectively to non-technical stakeholders.
  • Strong proficiency in SQL, Python.
  • Hands-on experience with big data technologies such as Spark, Kafka, or similar.
  • Expertise in cloud-based data solutions (AWS) and modern data warehousing (Databricks, etc.).
  • Proficiency in NoSQL databases (e.g., MongoDB, DynamoDB).
  • Experience designing and implementing microservices architecture for scalable and distributed data platforms.
  • Strong understanding of data governance, security, and compliance standards.
Preferred Qualifications
  • Familiarity with DevOps practices and CI/CD pipelines for data engineering.
  • Experience with ETL/ELT pipeline development and orchestration tools (Airflow, dbt, Dagster, etc.).

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