Senior Data Engineer — Databricks & DataOps Lead

Rearc; United States; Remote; USD 140,000 - 200,000; Full time

Job summary

Rearc is seeking a Senior Data Engineer to serve as the technical anchor on client Data engagements. You’ll design, build, and productionize data pipelines on Databricks and cloud platforms, setting technical standards and guiding clients through complex data challenges.

You’ll deliver hands-on coding, architect scalable lakehouse solutions, and mentor engineers while promoting knowledge sharing and DataOps practices across engagements.

Qualifications

• 6+ years of hands-on data engineering experience in production environments.
• Proficient in Spark runtime internals and performance tuning.
• Production-grade Python (Scala a strong plus) for Spark workloads.

Responsibilities

• Contribute to client data engagements as a senior technical contributor.
• Build and productionize scalable data pipelines and lakehouse architectures on Databricks and cloud platforms.
• Architect for scale and reliability with robust data models and reference architectures.
• Support technical delivery, identify blockers, and align with project timelines.
• Mentor junior and mid-level engineers through pairing and code reviews.
• Promote knowledge sharing via blogs, reference architectures, and internal guides.
• Champion DataOps practices including CI/CD, observability, and documentation.

Skills

6+ years data engineering

Spark expert

Python

Scala

Databricks

Cloud: AWS/Azure/GCP

DataOps mindset

ETL/ELT design

Data warehousing

Strong communicator

Tools

Databricks

Spark

Python

AWS/Azure/GCP

Job description

Rearc is seeking a Senior Data Engineer to serve as the technical anchor on client Data engagements. You’ll design, build, and productionize data pipelines on Databricks and cloud platforms, setting technical standards and guiding clients through complex data challenges.

You’ll deliver hands-on coding, architect scalable lakehouse solutions, and mentor engineers while promoting knowledge sharing and DataOps practices across engagements.