Bridgenext
Website:
bridgenext.com
Job details:
Company Overview
At Bridgenext, we engineer Growth Operating Systems. Most enterprises have spent millions on their revenue stack and still aren't seeing the growth they expected.They have tools that function, but no system that wins. We help growth-hungry companies close that gap by turning fragmented platforms, siloed teams, and disconnected data into one integrated Growth OS. More than a technology company or marketing agency, we're a global digital consultancy with experts in engineering, data, AI, creative and more.
Our teams are made up of experts who believe in engineering impact, starting with putting people at the center of everything we do. Every team member directly shapes our work, culture, and values. Nothing matters more to us than a kind, respectful, fulfilling environment that supports everyone. Our flexible, inclusive culture gives you the autonomy, resources, and opportunities to thrive.
Position Description
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
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