(US) Fullstack Data Engineer - Databricks

Codvo.ai-posted 3 months ago
Mid Level
Dallas, TX
101-250 employees
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We are looking for a highly skilled Full Stack Data Engineer with expertise in Databricks to design, develop, and optimize end-to-end data pipelines, data platforms, and analytics solutions. This role combines strong data engineering, cloud platform expertise, and software engineering skills to deliver scalable, production-grade solutions.

  • Design and develop ETL/ELT pipelines on Databricks (PySpark, Delta Lake, SQL).
  • Architect data models (batch and streaming) for analytics, ML, and reporting.
  • Optimize performance of large-scale distributed data processing jobs.
  • Implement CI/CD pipelines for Databricks workflows using GitHub Actions, Azure DevOps, or similar.
  • Build and maintain APIs, dashboards, or applications that consume processed data (full-stack aspect).
  • Collaborate with data scientists, analysts, and business stakeholders to deliver solutions.
  • Ensure data quality, lineage, governance, and security compliance.
  • Strong in PySpark, Delta Lake, Databricks SQL.
  • Experience with Databricks Workflows, Unity Catalog, and Delta Live Tables.
  • Proficiency in Python (mandatory), SQL (expert).
  • Exposure to Java/Scala (for Spark jobs).
  • Knowledge of APIs, microservices (FastAPI/Flask), or basic front-end (React/Angular) is a plus.
  • Proficiency with at least one cloud platform: Azure Databricks, AWS Databricks, or GCP Databricks.
  • Knowledge of cloud storage (ADLS, S3, GCS), IAM, networking.
  • Experience with Git, CI/CD tools (GitHub Actions, Azure DevOps, Jenkins).
  • Containerization (Docker, Kubernetes is a plus).
  • Data modeling (OLTP/OLAP).
  • Batch & streaming data processing (Kafka, Event Hub, Kinesis).
  • Data governance & compliance (Unity Catalog, Lakehouse security).
  • Experience with machine learning pipelines (MLflow, Feature Store).
  • Knowledge of data visualization tools (Power BI, Tableau, Looker).
  • Exposure to Graph databases (Neo4j) or RAG/LLM pipelines.
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