Lead Data Engineer: Data Platforms & Client Impact

Rearc; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Rearc is seeking a Lead Data Engineer to act as hands-on technical lead across client data engagements. You will own architecture and build, shaping scalable data pipelines and lakehouse architectures on Databricks and cloud platforms.

You will mentor engineers, drive technical delivery to meet commitments, and promote DataOps with automated testing, observability, and version control. This role requires 8+ years in data engineering and strong Spark/Python skills.

Qualifications

• 8+ years of hands-on data engineering experience.
• Expert-level in Apache Spark, including runtime internals, performance tuning, and optimization.
• Production-quality Python code; Scala a strong plus.
• Experience with Databricks, Delta Lake, Unity Catalog, and Workflows,
• Deep experience on at least two major cloud platforms (AWS/Azure/GCP).
• Leadership of data engineering projects end-to-end in client-facing contexts.
• DataOps mindset with CI/CD for data pipelines, testing, observability, IaC.

Responsibilities

• Lead Client Data Engagements on client projects, owning architecture and delivery.
• Build and productionize scalable data pipelines and lakehouse architectures on Databricks and cloud platforms.
• Architect for scale and reliability, translating complex requirements into robust designs.
• Drive technical delivery, manage scope and timelines, and coordinate with stakeholders.
• Mentor junior and mid-level data engineers through pairing and code reviews.
• Promote knowledge sharing through blogs, reference architectures, and guides.
• Champion DataOps practices with automated testing, observability, and CI/CD.

Skills

Data engineering

Apache Spark

Python

Scala

Databricks

Cloud platforms

CI/CD

ETL/ELT

Data modeling

Communication

Tools

Databricks Lakehouse

Delta Lake

Unity Catalog

Databricks Workflows

Job description

Rearc is seeking a Lead Data Engineer to act as hands-on technical lead across client data engagements. You will own architecture and build, shaping scalable data pipelines and lakehouse architectures on Databricks and cloud platforms.

You will mentor engineers, drive technical delivery to meet commitments, and promote DataOps with automated testing, observability, and version control. This role requires 8+ years in data engineering and strong Spark/Python skills.