Lead Databricks Data Engineer
At a glance
Senior Data Engineering role at Fa Ewjt Saasfaprod1. New York · full-time · no salary stated · we estimate $150,000–$205,000.
Fa Ewjt Saasfaprod1 has not published a salary for this role. The figure below is our estimate, not theirs.
dataroles summary, based on the employer's posting.
What you'll do
- Design Databricks Lakehouse platforms and build batch and streaming pipelines for large-scale data processing
- Set data models, ingestion patterns and transformations, then implement Delta Lake, Unity Catalog and governance controls
- Lead platform modernization, cloud migration, CI/CD and infrastructure-as-code work for data workloads
- Review architecture and code, set engineering standards, and mentor data engineers and technical leads
- Turn business needs into technical plans, coordinate delivery risks and resources, and present decisions to leaders
What you bring
- At least six years of hands-on Databricks work and seven years building data systems with Spark or PySpark
- Twelve or more years in IT, including experience designing enterprise data lake or lakehouse platforms
- A record of leading major data modernization efforts and making architecture decisions while guiding technical teams
- Strong experience with at least one of Azure, AWS or GCP
Who this fits
This senior lead role suits someone who combines hands-on Databricks engineering with architecture, team guidance and stakeholder coordination. The listed location is New York, with a hybrid arrangement.
From the employer
Job Description: Lead Databricks Data Engineer
Job Title: Lead Databricks Data Engineer
Experience: 10-12 Years
Employment Type: Full-Time
Job Summary
We are looking for a highly experienced Lead Databricks Data Engineer with 10-12 years of experience in Data Engineering, Big Data, and Cloud Data Platforms. The ideal candidate will lead the design, development, modernization, and optimization of enterprise-scale data platforms using Databricks Lakehouse architecture. This role requires strong technical leadership, solution architecture expertise, stakeholder management skills, and hands-on experience building scalable cloud-native data ecosystems.
The candidate will be responsible for defining data engineering standards, mentoring teams, driving modernization initiatives, and delivering high-performance data solutions that support analytics, AI/ML, and business intelligence initiatives.
Key Responsibilities Data Engineering & Architecture
- Design and implement enterprise-scale data platforms using Databricks Lakehouse architecture.
- Architect and develop scalable ETL/ELT data pipelines for batch and real-time processing.
- Define enterprise data models, ingestion frameworks, and transformation strategies.
- Lead the implementation of Delta Lake, Unity Catalog, and data governance frameworks.
- Build and optimize highly scalable Spark and PySpark solutions handling large-volume datasets.
- Drive cloud data platform modernization and migration initiatives.
Technical Leadership
- Serve as the technical lead for Databricks and Data Engineering programs.
- Establish engineering best practices, coding standards, and architecture guidelines.
- Conduct architecture reviews, code reviews, and solution assessments.
- Mentor and guide Data Engineers, Technical Leads, and junior team members.
- Collaborate with Enterprise Architects, Product Owners, Data Scientists, and Business Stakeholders.
Cloud & Platform Engineering
- Design cloud-native data solutions on Azure, AWS, or GCP.
- Implement secure, scalable, and resilient data architectures.
- Optimize costs, performance, reliability, and availability of data platforms.
- Lead CI/CD and Infrastructure-as-Code implementation for data workloads.
- Define and implement monitoring, observability, and operational excellence practices.
Governance & Data Quality
- Establish data governance, lineage, cataloging, and security controls.
- Ensure compliance with organizational and regulatory requirements.
- Implement data quality frameworks and monitoring solutions.
- Collaborate with security and compliance teams to enforce data protection standards.
Stakeholder & Delivery Management
- Partner with business leaders to understand strategic data requirements.
- Translate business requirements into scalable technical solutions.
- Drive delivery planning, risk management, and resource coordination.
- Present architecture decisions and technical recommendations to leadership teams.
Required Technical Skills Databricks
- Databricks Lakehouse Platform
- Databricks Workflows
- Delta Lake
- Unity Catalog
- Structured Streaming
- Databricks SQL
- Delta Live Tables (DLT)
- Databricks Asset Bundles
Big Data Technologies
- Apache Spark
- PySpark
- Spark SQL
- Kafka
- Real-Time Streaming Architectures
- Data Lake & Lakehouse Architectures
Cloud Platforms (Strong Experience in One or More) Azure
- Azure Databricks
- Azure Data Factory (ADF)
- ADLS Gen2
- Azure Synapse Analytics
- Azure Key Vault
- Event Hub
AWS
- S3
- EMR
- Glue
- Redshift
- Lambda
- IAM
GCP
- BigQuery
- Cloud Storage
- Dataflow
- Pub/Sub
- Composer
Programming
- Python
- SQL
- PySpark
- Scala (Preferred)
- Shell Scripting
DevOps & Automation
- Git
- Azure DevOps
- GitHub Actions
- Jenkins
- Terraform
- Docker
- Kubernetes
Data Warehousing
- Dimensional Modeling
- Star & Snowflake Schema
- Data Vault
- Master Data Management
- Data Governance Frameworks
Required Experience
- 12-15 years of overall IT experience.
- Minimum 6+ years of hands-on Databricks experience.
- 7+ years of experience in Spark/PySpark-based data engineering.
- Experience designing enterprise-scale Data Lake and Lakehouse solutions.
- Strong experience leading large data transformation and modernization programs.
- Experience handling structured, semi-structured, and unstructured data processing.
- Experience supporting analytics, AI/ML, and reporting ecosystems.
- Proven experience managing technical teams and driving architectural decisions.
Preferred Qualifications
- Bachelor's or Master's Degree in Computer Science, Engineering, Information Technology, or related field.
- Databricks Certified Data Engineer Professional.
- Azure Data Engineer Associate.
- AWS Data Analytics Specialty.
- Google Professional Data Engineer Certification.
- TOGAF or Cloud Architecture certifications are a plus.
Nice to Have
- MLOps and AI Engineering exposure.
- Snowflake and Databricks integration experience.
- Microsoft Fabric exposure.
- Data Mesh implementation experience.
- Data Governance tools such as Collibra, Alation, or Informatica.
- Expertise in Data Quality frameworks such as Great Expectations or Deequ.
Key Competencies
- Solution Architecture
- Technical Leadership
- Stakeholder Management
- Program Delivery
- Data Strategy & Governance
- Team Mentoring & Coaching
- Problem Solving & Decision Making
- Communication & Executive Presentation Skills
- Performance Optimization
- Innovation & Continuous Improvement
Technology Classification
Technology Tower: Data & AI
Practice Area: Data Engineering
Primary Skill: Databricks Lakehouse Engineering
Secondary Skills: Apache Spark, PySpark, Cloud Data Platforms (Azure/AWS/GCP), Data Architecture, Data Governance, MLOps.
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