(US) Fullstack Data Engineer - Databricks

Job Description:

Data Engineer – Databricks (US Citizens or GC holders only)

Role Overview

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.

Key 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.

Required Skills & Qualifications

• Core Databricks Skills:
• Strong in PySpark, Delta Lake, Databricks SQL.
• Experience with Databricks Workflows, Unity Catalog, and Delta Live Tables.
• Programming & Full Stack:
• 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.
• Cloud Platforms:
• Proficiency with at least one: Azure Databricks, AWS Databricks, or GCP Databricks.
• Knowledge of cloud storage (ADLS, S3, GCS), IAM, networking.
• DevOps & CI/CD:
• Git, CI/CD tools (GitHub Actions, Azure DevOps, Jenkins).
• Containerization (Docker, Kubernetes is a plus).
• Data Engineering Foundations:
• Data modeling (OLTP/OLAP).
• Batch & streaming data processing (Kafka, Event Hub, Kinesis).
• Data governance & compliance (Unity Catalog, Lakehouse security).

Nice-to-Have

• 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.

Qualifications

• 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.

Soft Skills

• Strong problem-solving and analytical skills.
• Ability to work in fusion teams (business + engineering + AI/ML).
• Clear communication and documentation abilities.

About Us

At Codvo, we are committed to building scalable, future-ready data platforms that power business impact. We believe in a culture of innovation, collaboration, and growth, where engineers can experiment, learn, and thrive. Join us to be part of a team that solves complex data challenges with creativity and cutting-edge technology.