Data Engineer (Databricks)

wizeline; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Wizeline is seeking a data engineer to design and run scalable data infrastructure powering analytics, AI, and digital platforms. You will shape ETL/ELT pipelines, data lake/warehouse architectures, and large distributed systems handling high-volume datasets with emphasis on quality and governance.

You will collaborate with software engineers and data teams, drive CI/CD, and apply security best practices while leveraging Databricks on Azure and modern orchestration tools.

Qualifications

• Proven track record designing and operating scalable ETL/ELT pipelines and data warehouse/lake architectures.
• Hands-on experience with distributed computing systems and large, high-volume datasets.
• Strong data modeling skills (dimensional modeling, star schemas, or data vault).
• Advanced SQL and database design/optimization for relational and non-relational databases.
• Experience with streaming and batch processing, orchestration tooling, and cloud patterns.
• Knowledge of data governance, security best practices, and CI/CD/version control.
• Systems-thinking approach with focus on data quality, accuracy, reliability.
• Experience with Databricks on Azure, and data contracts concepts.

Responsibilities

• Build, maintain, and optimize scalable data pipelines for high-throughput datasets.
• Design and evolve data warehouse and data lake architectures using established patterns.
• Operate distributed computing environments for large-scale data processing.
• Apply governance, cataloging, and security practices to keep assets trustworthy.
• Collaborate with software engineers and data consumers to translate needs both ways.
• Advance CI/CD, version control, and workflow orchestration across data products.

Skills

ETL/ELT pipelines

Data modeling

SQL proficiency

Distributed computing

Data governance & security

CI/CD practices

Collaboration with engineers

Systems thinking

Tools

Databricks (Azure)

Unity Catalog

Data contracts

Data lake architectures

Data warehouse modeling

Airflow

Azure cloud

Job description

Role overview

This position centers on designing and running the scalable data infrastructure that powers analytics, AI, and digital product platforms. The engineer will shape ETL/ELT pipelines, data lake and warehouse architectures, and large-scale distributed systems that move and refine high-volume datasets reliably. It is a hands-on role that bridges data producers and downstream consumers, keeping quality, governance, and trust front of mind.

Responsibilities

• Build, maintain, and optimize scalable batch and streaming data pipelines handling high-throughput datasets
• Design and evolve data warehouse and data lake architectures using established modeling patterns
• Operate distributed computing environments that support reliable, large-scale data processing
• Apply governance, cataloging, and security practices (such as Unity Catalog and data contracts) to keep assets trustworthy and discoverable
• Collaborate with software engineers producing data and the teams consuming it, translating needs in both directions
• Drive CI/CD, version control, and workflow orchestration hygiene across data engineering deliverables

Requirements

• Proven track record designing and operating scalable ETL/ELT pipelines and warehouse or lake architectures
• Hands-on experience with distributed computing systems and large, high-volume datasets
• Practical knowledge of data modeling approaches such as dimensional modeling, star schemas, or data vault
• Advanced SQL plus strong relational and non-relational database design and optimization skills
• Familiarity with stream and batch processing paradigms, orchestration tooling, and cloud infrastructure patterns
• Grounding in data governance, security best practices, and CI/CD/version control principles
• Systems-thinking mindset combined with meticulous attention to data quality, accuracy, and reliability
• Working experience with Databricks (including Asset Bundles and Unity Catalog) on Azure, along with Data-as-a-Service and data contract concepts

Nice to have

• Comfort using AI tooling to automate workflows, accelerate research, or augment day-to-day data engineering tasks