Lead Data Engineer - Lakehouse Migration (Azure/Databricks)

Royper Royco LLC

United States

Remote

USD 110,000 - 145,000

Full time

6 days ago
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Benefits offered by this job

Fully remote
Tech & Education Stipend
Comprehensive Benefits Package
Company Match 401(k) plan
Flexible PTO, Paid Holidays

Job summary

Oddball is seeking a Lead Data Engineer to drive the migration of legacy data warehouse and pipelines to an Azure Databricks lakehouse. You will set technical direction, mentor engineers, and collaborate with product, QA, and platform teams from a fully remote setup in the United States.

The role emphasizes leading migrations, hands-on Databricks and Spark work, and ensuring data accuracy through robust reconciliation and quality gates.

Qualifications

  • Leadership experience on large migration or modernization programs.
  • Deep experience with Databricks or Spark and Azure Data Factory.
  • Strong SQL and Python skills.
  • Experience moving legacy ETL and data warehouse workloads to the cloud.

Responsibilities

  • Set the technical approach for migrating data and pipelines onto the lakehouse.
  • Guide migration teams on patterns, reconciliation, and quality gates.
  • Design reusable migration pipelines using Azure Data Factory and Azure Databricks.
  • Define how source and target data are reconciled to confirm accuracy.
  • Resolve the hardest pipeline and performance problems.
  • Mentor data engineers and review their work.
  • Partner with product, QA, and platform teams.

Skills

Databricks/Spark
Azure Data Factory
SQL
Python
Data migration to cloud
Data engineering leadership

Education

Bachelor's Degree

Job description

Oddball is seeking a Lead Data Engineer to drive the migration of legacy data warehouse and pipelines to an Azure Databricks lakehouse. You will set technical direction, mentor engineers, and collaborate with product, QA, and platform teams from a fully remote setup in the United States.

The role emphasizes leading migrations, hands-on Databricks and Spark work, and ensuring data accuracy through robust reconciliation and quality gates.

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