Databricks Engineer

1 Month ago • 5 Years +

Job Summary

Job Description

ShyftLabs is looking for a skilled Databricks Engineer to design, develop, and optimize big data solutions using the Databricks Unified Analytics Platform. This role involves using Apache Spark, SQL, Python, and cloud platforms (AWS/Azure/GCP). The ideal candidate will work with different teams to provide data-driven insights and build scalable data architectures. Responsibilities include designing data pipelines, developing ETL workflows, using Apache Spark for data processing, implementing data governance, optimizing data lakehouse architectures, collaborating with data scientists and engineers for AI/ML workflows, monitoring Databricks, and automating workflows. The company offers a competitive salary and encourages employee growth through learning resources.

Must have:

Experience with Databricks and Apache Spark.
Proficiency in SQL, Python, or Scala.
Experience with cloud platforms (AWS, Azure, or GCP).
Knowledge of ETL frameworks and Delta Lake.
Experience with CI/CD tools and DevOps practices.
Understanding of data security and governance.
Bachelor’s or Master’s degree in related field.

Good to have:

Databricks certifications (e.g., Databricks Certified Data Engineer).
Experience with MLflow, Feature Store, or Databricks SQL.
Exposure to Kubernetes, Docker, and Terraform.
Experience with streaming data architectures (Kafka, Kinesis, etc.).
Understanding of business intelligence and reporting tools.
Prior experience with retail, e-commerce, or ad-tech data platforms.

Perks:

Competitive salary.
Strong insurance package.
Extensive learning and development resources.

15 skills required

15 skills required for this role

Add these skills to join the top 1% applicants for this job

spark

sql

ci-cd

azure

aws

python

data-analytics

scala

kubernetes

looker

docker

terraform

tableau

power-bi

business-intelligence

Job Details

Position Overview: ShyftLabs is seeking a skilled Databricks Engineer to support in designing, developing, and optimizing big data solutions using the Databricks Unified Analytics Platform. This role requires strong expertise in Apache Spark, SQL, Python, and cloud platforms (AWS/Azure/GCP). The ideal candidate will collaborate with cross-functional teams to drive data-driven insights and ensure scalable, high-performance data architectures.
ShyftLabs is a growing data product company that was founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation.

Job Responsiblities

Design, implement, and optimize big data pipelines in Databricks.
Develop scalable ETL workflows to process large datasets.
Leverage Apache Spark for distributed data processing and real-time analytics.
Implement data governance, security policies, and compliance standards.
Optimize data lakehouse architectures for performance and cost-efficiency.
Collaborate with data scientists, analysts, and engineers to enable advanced AI/ML workflows.
Monitor and troubleshoot Databricks clusters, jobs, and performance bottlenecks.
Automate workflows using CI/CD pipelines and infrastructure-as-code practices.
Ensure data integrity, quality, and reliability in all pipelines.

Basic Qualifications

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
5+ years of hands-on experience with Databricks and Apache Spark.
Proficiency in SQL, Python, or Scala for data processing and analysis.
Experience with cloud platforms (AWS, Azure, or GCP) for data engineering.
Strong knowledge of ETL frameworks, data lakes, and Delta Lake architecture.
Experience with CI/CD tools and DevOps best practices.
Familiarity with data security, compliance, and governance best practices.
Strong problem-solving and analytical skills with an ability to work in a fast-paced environment.

Preferred Qualifications

Databricks certifications (e.g., Databricks Certified Data Engineer, Spark Developer).
Hands-on experience with MLflow, Feature Store, or Databricks SQL.
Exposure to Kubernetes, Docker, and Terraform.
Experience with streaming data architectures (Kafka, Kinesis, etc.).
Strong understanding of business intelligence and reporting tools (Power BI, Tableau, Looker).
Prior experience working with retail, e-commerce, or ad-tech data platforms.

We are proud to offer a competitive salary alongside a strong insurance package. We pride ourselves on the growth of our employees, offering extensive learning and development resources.