Senior Data Engineer: Lead Data Pipelines on Azure

Harvey Nash

Charlotte (NC)

Hybrid

USD 103,000 - 117,000

Full time

5 days ago
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Job summary

Harvey Nash is seeking a Sr. Data Engineer in Charlotte, NC (Hybrid: 2 days a week). This contract-to-hire role requires strong hands-on data engineering skills, including Python, PySpark, SQL, and Databricks, plus experience with Azure cloud data platforms and leadership.

The team will focus on enterprise-scale data solutions, data lakes, and ETL/ELT pipelines. Candidates should be prepared for live coding in PySpark and SQL during the technical interview, and mentoring responsibilities with

Qualifications

  • Strong hands-on experience with Python, PySpark, SQL and Databricks.
  • Strong experience with Azure/cloud data platforms.
  • Proven technical/team leadership experience.
  • Experience designing and implementing enterprise-scale data solutions.
  • Strong understanding of DataLakes, ETL/ELT, data modeling and performance tuning.
  • Experience with CI/CD and production data pipelines.
  • Ability to mentor engineers, conduct code reviews and provide technical direction.
  • Experience with streaming technologies such as Kafka/Kinesis is a plus.
  • Experience in regulated/enterprise environments is preferred.

Responsibilities

  • The technical interview will include live coding, with a strong focus on PySpark and SQL, along with experience- and scenario-based technical questions.
  • A reasonable, good faith estimate of the minimum and maximum hourly wage for this position is $75/Hr. to $85/hr.

Skills

Python
PySpark
SQL
Databricks
Azure cloud
CI/CD
Kafka/Kinesis
Mentoring
Code reviews
Data modeling

Tools

Databricks

Job description

Harvey Nash is seeking a Sr. Data Engineer in Charlotte, NC (Hybrid: 2 days a week). This contract-to-hire role requires strong hands-on data engineering skills, including Python, PySpark, SQL, and Databricks, plus experience with Azure cloud data platforms and leadership.

The team will focus on enterprise-scale data solutions, data lakes, and ETL/ELT pipelines. Candidates should be prepared for live coding in PySpark and SQL during the technical interview, and mentoring responsibilities with

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