Lead Databricks Engineer

  • Dallas, TX
  • Posted 60+ days ago | Updated 23 hours ago

Overview

Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - 12 Month(s)
Unable to Provide Sponsorship

Skills

Databricks
Python
Pyspark
AWS Cloud
Apache Kafka
Unity catalog
cost modernization
Finops Project

Job Details

Must be local to TX

Location: Addison, Texas (Hybrid/On-site 3 days per week)

Duration: longterm contract

 

Job Summary:

As a Databricks Lead, you will be a critical member of our data engineering team, responsible for designing, developing, and optimizing our data pipelines and platforms on Databricks, primarily leveraging AWS services. You will play a key role in implementing robust data governance with Unity Catalog and ensuring cost-effective data solutions. This role requires a strong technical leader who can mentor junior engineers, drive best practices, and contribute hands-on to complex data challenges.

 

Responsibilities:

* Databricks Platform Leadership:

   * Lead the design, development, and deployment of large-scale data solutions on the Databricks platform.

   * Establish and enforce best practices for Databricks usage, including notebook development, job orchestration, and cluster management.

   * Stay abreast of the latest Databricks features and capabilities, recommending and implementing improvements.

* Data Ingestion and Streaming (Kafka):

   * Architect and implement real-time and batch data ingestion pipelines using Apache Kafka for high-volume data streams.

   * Integrate Kafka with Databricks for seamless data processing and analysis.

   * Optimize Kafka consumers and producers for performance and reliability.

* Data Governance and Management (Unity Catalog):

   * Implement and manage data governance policies and access controls using Databricks Unity Catalog.

   * Define and enforce data cataloging, lineage, and security standards within the Databricks Lakehouse.

   * Collaborate with data governance teams to ensure compliance and data quality.

* AWS Cloud Integration:

   * Leverage various AWS services (S3, EC2, Lambda, Glue, etc.) to build a robust and scalable data infrastructure.

   * Manage and optimize AWS resources for Databricks workloads.

   * Ensure secure and compliant integration between Databricks and AWS.

* Cost Optimization:

   * Proactively identify and implement strategies for cost optimization across Databricks and AWS resources.

   * Monitor DBU consumption, cluster utilization, and storage costs, providing recommendations for efficiency gains.

   * Implement autoscaling, auto-termination, and right-sizing strategies to minimize operational expenses.

* Technical Leadership & Mentoring:

   * Provide technical guidance and mentorship to a team of data engineers.

   * Conduct code reviews, promote coding standards, and foster a culture of continuous improvement.

   * Lead technical discussions and decision-making for complex data engineering problems.

* Data Pipeline Development & Optimization:

   * Develop, test, and maintain robust and efficient ETL/ELT pipelines using PySpark/Spark SQL.

   * Optimize Spark jobs for performance, scalability, and resource utilization.

   * Troubleshoot and resolve complex data pipeline issues.

* Collaboration:

   * Work closely with data scientists, analysts, and other engineering teams to understand data requirements and deliver solutions.

   * Communicate technical concepts effectively to both technical and non-technical stakeholders.

 

Qualifications:

* Bachelor's or Master's degree in Computer Science, Data Engineering, or a related quantitative field.

* 7+ years of experience in data engineering, with at least 3+ years in a lead or senior role.

* Proven expertise in designing and implementing data solutions on Databricks.

* Strong hands-on experience with Apache Kafka for real-time data streaming.

* In-depth knowledge and practical experience with Databricks Unity Catalog for data governance and access control.

* Solid understanding of AWS cloud services and their application in data architectures (S3, EC2, Lambda, VPC, IAM, etc.).

* Demonstrated ability to optimize cloud resource usage and implement cost-saving strategies.

* Proficiency in Python and Spark (PySpark/Spark SQL) for data processing and analysis.

* Experience with Delta Lake and other modern data lake formats.

* Excellent problem-solving, analytical, and communication skills.

Added Advantage (Bonus Skills):

* Experience with Apache Flink for stream processing.

* Databricks certifications.

* Experience with CI/CD pipelines for Databricks deployments.

* Knowledge of other cloud platforms (Azure, Google Cloud Platform) is a plus.

 

 

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.