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Databricks Data Engineer Jobs in Charlotte, NC (NOW HIRING)

Incedo Inc

Data Engineer AWS OpenSearch

Fort Mill, SC · On-site

$100K - $120K/yr

Experience with Snowflake, Redshift, or Databricks. * Experience working with large-scale distributed systems. * AWS Certification (Solutions Architect, Data Analytics, or Developer) is a plus.

Entech

Big Data Architect

Charlotte, NC · On-site

$62.25 - $80/hr

Experience: 15+ years in data engineering or enterprise architecture, with specific background in ... Hands-on expertise with Apache Spark, Kafka, Hive, or Databricks. * Data Modeling: Mastery of ...

Compunnel

Palantir Data Engineer Level III/IV

Charlotte, NC · On-site

$56.25 - $75.50/hr

Job Summary We are seeking a Palantir Data Engineer Level III/IV with strong experience in data ... The ideal candidate will have hands-on experience with Palantir, AWS, Databricks, Apache Hive ...

Euclid Innovations

Senior Data Engineer

Fort Mill, SC · On-site

$99K - $134K/yr

... Databricks for orchestration and transformation • Basic understanding of AI/ML data pipeline ... DevOps. Founded in 2009, the company is headquartered in Charlotte, USA, with a team of 501-1000 ...

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All JobsDatabricks Data Engineer JobsDatabricks Data Engineer Jobs in Charlotte, NC

Databricks Data Engineer information

See Charlotte, NC salary details

$43.5K

$126.7K

$173.4K

How much do databricks data engineer jobs pay per year?

As of Sep 6, 2026, the average yearly pay for databricks data engineer in Charlotte, NC is $126,696.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,800.00 and $134,300.00 per year, depending on experience, location, and employer.

What is a Databricks data engineer?

A Databricks Data Engineer is responsible for designing, building, and maintaining scalable data pipelines on the Databricks platform. They work with Apache Spark, Delta Lake, and cloud services to process large datasets efficiently. Their role involves data ingestion, transformation, optimization, and ensuring data quality for analytics and machine learning. Additionally, they collaborate with data scientists, analysts, and business teams to deliver reliable data solutions.

What does a Databricks data engineer do?

A typical day for a Databricks Data Engineer involves developing and maintaining scalable data pipelines, optimizing big data workflows using Spark, and collaborating with data scientists, analysts, and other engineers. You will regularly work within cloud environments to manage and process large datasets, conduct troubleshooting, and ensure data reliability and performance. Daily tasks may also include writing code, participating in team meetings, and implementing best practices for data security and governance. This role is highly collaborative, requiring frequent communication to align on project goals and address any technical challenges. The dynamic, project-based structure helps expand your skills and offers growth opportunities into senior engineering or data architecture roles.

What are the key skills and qualifications needed to thrive as a Databricks data engineer?

To thrive as a Databricks Data Engineer, you need strong expertise in data engineering concepts, big data processing, and programming languages such as Python, Scala, or SQL, often supported by a degree in computer science or a related field. Proficiency in Databricks, Apache Spark, cloud platforms (like AWS, Azure, or GCP), and relevant certifications such as Databricks Certified Data Engineer are highly valued. Effective problem-solving, collaboration, and clear communication skills help engineers work efficiently within cross-functional teams. These skills are essential for designing scalable data pipelines, ensuring data quality, and delivering actionable analytics in dynamic business environments.

How much does a Databricks data engineer make?

A Databricks Data Engineer typically earns between $90,000 and $150,000 annually, depending on experience, location, and certifications. Senior roles or those with advanced skills in Spark, cloud platforms, and data pipeline development can earn higher salaries.

Is a Databricks Data Engineer in demand?

Databricks Data Engineers are in high demand due to the increasing adoption of cloud-based data platforms and big data processing. Skills in Apache Spark, cloud environments, and data pipeline development are highly sought after, leading to strong job growth in this field.

What are the most commonly searched types of Databricks Data Engineer jobs in Charlotte, NC?

The most popular types of Databricks Data Engineer jobs in Charlotte, NC are:

What are popular job titles related to Databricks Data Engineer jobs in Charlotte, NC?

For Databricks Data Engineer jobs in Charlotte, NC, the most frequently searched job titles are:

What job categories do people searching Databricks Data Engineer jobs in Charlotte, NC look for?

The top searched job categories for Databricks Data Engineer jobs in Charlotte, NC are:

What cities near Charlotte, NC are hiring for Databricks Data Engineer jobs?

Cities near Charlotte, NC with the most Databricks Data Engineer job openings:

Databricks Data Engineer jobs near you
Infographic showing various Databricks Data Engineer job openings in Charlotte, NC as of August 2026, with employment types broken down into 87% Full Time, and 13% Contract. Highlights an 89% In-person, 2% Hybrid, and 9% Remote job distribution, with an average salary of $126,696 per year, or $60.9 per hour.

Hiring: GCP Data Engineer at Charlotte, NC

Realtech Services

Charlotte, NC • On-site

$111K - $134K/yr

Contractor

Re-posted 7 days ago

Job description


 

Position: GCP Data Engineer

Location: Charlotte, NC

Duration: Long-Term Contract

Skill Set:

  • Extensive hands-on experience with BigQuery, Cloud Storage, Cloud SQL, Cloud Dataflow and Cloud Composer.
  • Experience with performance monitoring and pipeline observability using GCP-native tools or third-party solutions
  • Proficiency with GCP resource management and workload optimization and cost control strategies
  • Familiarity with BigQuery for large-scale analytics and schema evolution
  • Knowledge of data lakehouse patterns using Apache Iceberg or similar technologies
  • Well-versed in PySpark, Pandas, and Polars for data processing
  • Experience using Jira and Confluence for agile workflows
  • Experience with interactive notebook environments (Jupyter, Zepplin, databricks or similar) for data engineering development