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Senior Data Engineer

Architect high-performance batch and real-time data systems, mentor engineers, and accelerate analytics and AI initiatives on modern cloud platforms.

RemoteFull-timeSenior levelSpark / PySpark / SQLGCP / AWS

The Role

We are seeking a Senior Data Engineer with deep expertise in Spark/PySpark/SQL to join our data team.

This is a hands-on technical role for someone passionate about building scalable data systems, mentoring engineers, and shaping data strategy.

You will architect systems that power high-performance data processing, enable advanced analytics, and accelerate AI initiatives.

What You'll Do

  • Design and evolve scalable, distributed data infrastructure across cloud platforms including GCP and AWS.
  • Build and maintain real-time and batch data processing pipelines supporting AI/ML workloads, consumer applications, and analytics.
  • Develop and manage integrations with third-party e-commerce platforms to expand the data ecosystem.
  • Ensure data availability, reliability, and quality through monitoring and automated auditing.
  • Partner with engineering, AI, and product teams on data solutions for business-critical needs.
  • Mentor and support data engineers, establishing best practices and code quality standards.

Ideal Background

  • Bachelor's degree in Computer Science or a related field, or equivalent practical experience.
  • 5+ years of software development and data engineering experience with ownership of production-grade data infrastructure.
  • Deep expertise scaling Spark, PySpark, and SQL in production, including Databricks or DataProc on GCP.
  • Strong understanding of distributed computing and modern data modeling for scalable systems.
  • Proficient in Python with experience implementing software engineering best practices.
  • Hands-on experience with both relational and NoSQL systems including MySQL, MongoDB, and Elasticsearch.
  • Strong communicator with experience influencing cross-functional stakeholders.

Nice to Have

  • Experience with job orchestration and containerization tools such as Airflow and Docker.
  • Experience working with vector stores and knowledge graphs.
  • Experience working in early-stage, high-growth environments.
  • Familiarity with MLOps pipelines and integrating ML models into data workflows.
  • A proactive, problem-solving mindset with a passion for innovative solutions.