Lead Data Engineer (Databricks)

Bridgenext is a digital consulting services leader that helps clients innovate with intention and realize their digital aspirations by creating digital products, experiences, and solutions around what real people need. Our global consulting and delivery teams facilitate highly strategic digital initiatives through digital product engineering, automation, data engineering, and infrastructure modernization services, while elevating brands through digital experience, creative content, and customer data analytics services.

Don't just work, thrive. At Bridgenext, you have an opportunity to make a real difference - driving tangible business value for clients, while simultaneously propelling your own career growth. Our flexible and inclusive work culture provides you with the autonomy, resources, and opportunities to succeed. 

As a Lead Data Engineer specializing in Databricks, you will be a key player in designing, developing, and optimizing our enterprise transportation client's next-generation data platform. You will lead a team of data engineers, providing technical guidance, mentorship, and ensuring the scalable, and high-performance data solutions.

Key Responsibilities:

• Technical Leadership:
• Lead the design, development, and implementation of scalable and reliable data pipelines using Databricks, Spark, and other relevant technologies
• Define and enforce data engineering best practices, coding standards, and architectural patterns
• Provide technical guidance and mentorship to junior and mid-level data engineers
• Conduct code reviews and ensure the quality, performance, and maintainability of data solutions
• Databricks Expertise:
• Architect and implement data solutions on the Databricks platform, including Databricks Lakehouse, Delta Lake, and Unity Catalog
• Optimize Spark workloads for performance and cost efficiency on Databricks
• Develop and manage Databricks notebooks, jobs, and workflows
• Proficiently use Databricks features such as Delta Live Tables (DLT), Photon, and SQL Analytics
• Pipeline Development & Operations:
• Develop, test, and deploy robust ETL/ELT pipelines for data ingestion, transformation, and loading from various sources (e.g., relational databases, APIs, streaming data)
• Implement monitoring, alerting, and logging for data pipelines to ensure operational excellence
• Troubleshoot and resolve complex data-related issues
• Collaboration & Communication:
• Work closely with cross-functional teams including product managers, data scientists, and software engineers
• Communicate complex technical concepts clearly to both technical and non-technical stakeholders
• Stay updated with industry trends and emerging technologies in data engineering and Databricks

Must Have Skills:

• Experience:
  • 8+ years of experience in data engineering, with at least 2-3 years in a lead or senior capacity
  • Proven experience designing and building large-scale data platforms
• Primary Skills - Databricks:
  • Extensive hands-on experience with Databricks platform, including Databricks Workspace, Spark on Databricks, Delta Lake, and Unity Catalog
  • Strong proficiency in optimizing Spark jobs and understanding Spark architecture
  • Experience with Databricks features like Delta Live Tables (DLT), Photon, and Databricks SQL Analytics
• Programming:
  • Expertise in Python (PySpark) is essential
  • Strong proficiency in SQL
• Data Warehousing/Lakes:
  • Deep understanding of data warehousing concepts, dimensional modeling, and data lake architectures
  • Experience with various data storage formats (Parquet, ORC, JSON, CSV)
• Cloud Platforms:
  • Experience with at least one major cloud platform (AWS, Azure, or GCP) and their data-related services (e.g., S3, ADLS, GCS, EC2, Azure VMs, Google Compute Engine)
• Tools & Technologies:
  • Experience with version control systems (Git)
  • Familiarity with CI/CD pipelines for data solutions
  • Knowledge of workflow orchestration tools (e.g., Apache Airflow, Databricks Workflows)
• Education:
  • Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field

Preferred Skills:

• Experience with stream processing technologies (e.g., Kafka, Kinesis) is a plus
• Contributions to open-source data projectsExperience with Scala or Java is a plus
• Databricks certifications (e.g., Databricks Certified Data Engineer Associate/Professional)
• Experience with MLOps and integrating data pipelines with machine learning workflows

Professional Skills:

• Excellent problem-solving and analytical skills
• Strong leadership, communication, and interpersonal skills
• Ability to work independently and as part of a team in a fast-paced environment

Bridgenext is an Equal Opportunity Employer