(US) Fullstack Data Engineer - Databricks

Codvo.ai•Dallas, TX

22h

About The Position

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.

Requirements

Strong in PySpark, Delta Lake, Databricks SQL.
Experience with Databricks Workflows, Unity Catalog, and Delta Live Tables.
Python (mandatory), SQL (expert).
Exposure to Java/Scala (for Spark jobs).
Proficiency with at least one: Azure Databricks, AWS Databricks, or GCP Databricks.
Knowledge of cloud storage (ADLS, S3, GCS), IAM, networking.
Git, CI/CD tools (GitHub Actions, Azure DevOps, Jenkins).
Data modeling (OLTP/OLAP).
Batch & streaming data processing (Kafka, Event Hub, Kinesis).
Data governance & compliance (Unity Catalog, Lakehouse security).
Bachelor’s or Master’s in Computer Science, Data Engineering, or related field.
4–7 years of experience in data engineering, with at least 2 years on Databricks.
Strong problem-solving and analytical skills.
Ability to work in fusion teams (business + engineering + AI/ML).
Clear communication and documentation abilities.

Nice To Haves

Knowledge of APIs, microservices (FastAPI/Flask), or basic front-end (React/Angular) is a plus.
Containerization (Docker, Kubernetes is a plus).
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.

Responsibilities

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.

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