Data Team Lead

Incedo Inc.; San Rafael (CA); Hybrid; USD 130,000 - 150,000; Full time

Benefits offered by this job

Medical insurance

Vision insurance

401(k)

Job summary

A leading technology consulting firm is seeking a Databricks Data Lead in San Rafael, CA. The ideal candidate will have 8-12 years of experience in data engineering and strong hands-on expertise with the Databricks Lakehouse Platform, Apache Spark, and PySpark. This role requires designing and optimizing cloud-native data platforms and involves direct onsite collaboration with client stakeholders. The position offers opportunities for growth from data engineering into architecture responsibilities.

Qualifications

• 8-12 years in data engineering or analytics engineering.
• Strong hands-on experience with the Databricks Lakehouse Platform.
• Ability to work onsite 3 days/week in San Rafael, CA.

Responsibilities

• Design, develop, and maintain data pipelines using Databricks.
• Implement medallion lakehouse architectures and data quality.
• Collaborate onsite to gather technical requirements.

Skills

Databricks Lakehouse Platform

Apache Spark internals

Advanced Python (PySpark)

SQL development

Data warehousing concepts

Cloud data platforms (AWS, Azure, GCP)

Delta Lake

Git-based version control

Verbal and written communication

Education

Bachelor’s degree in Computer Science, Engineering, or related field

Tools

Databricks

Apache Airflow

Azure Data Factory

Job description

Databricks Data Lead

location: San Rafael, CA - Bay Area, California (Local candidates only)

hybrid: 3 days/week onsite at client office in San Rafael, CA

Experience Level

• 8-12 years in data engineering, analytics engineering, or distributed data systems

Role Overview

We are seeking a Databricks Data Lead to support the design, implementation, and optimization of cloud-native data platforms built on the Databricks Lakehouse Architecture. This is a hands‑on, engineering‑driven role requiring deep experience with Apache Spark, Delta Lake, and scalable data pipeline development, combined with early-stage architectural responsibilities.

The role involves close onsite collaboration with client stakeholders, translating analytical and operational requirements into robust, high‑performance data architectures, while adhering to best practices for data modeling, governance, reliability, and cost efficiency.

Key Responsibilities

• Design, develop, and maintain batch and near‑real‑time data pipelines using Databricks, PySpark, and Spark SQL
• Implement medallion (Bronze/Silver/Gold) lakehouse architectures, ensuring proper data quality, lineage, and transformation logic across layers
• Build and manage Delta Lake tables, including schema evolution, ACID transactions, time travel, and optimized data layouts
• Apply performance optimization techniques such as partitioning strategies, Z‑Ordering, caching, broadcast joins, and Spark execution tuning
• Support dimensional and analytical data modeling for downstream consumption by BI tools and analytics applications
• Assist in defining data ingestion patterns (batch, incremental loads, CDC, and streaming where applicable)
• Troubleshoot and resolve pipeline failures, data quality issues, and Spark job performance bottlenecks
• Collaborate onsite with client data engineers, analysts, and business stakeholders to gather technical requirements, validate implementation approaches, and maintain technical documentation covering data flows, transformation logic, table designs, and architectural decisions
• Contribute to code reviews, CI/CD practices, and version control workflows to ensure maintainable and production‑grade solutions

Required Skills & Qualifications

• Strong hands‑on experience with Databricks Lakehouse Platform
• Deep working knowledge of Apache Spark internals, including:
  • Spark SQL
  • DataFrames/Datasets
  • Shuffle behavior and execution plans
• Advanced Python (PySpark) and SQL development skills
• Solid understanding of data warehousing concepts, including:
  • Star and snowflake schemas
  • Analytical vs operational workloads
• Experience working with cloud data platforms on AWS, Azure, or GCP
• Practical experience with Delta Lake, including:
  • Schema enforcement and evolution
  • Data compaction and optimization
• Proficiency with Git‑based version control and collaborative development workflows
• Strong verbal and written communication skills for client‑facing technical discussions
• Ability and willingness to work onsite 3 days/week in San Rafael, CA

Nice‑to‑Have Skills

• Exposure to Databricks Unity Catalog, data governance, and access control models
• Experience with Databricks Workflows, Apache Airflow, or Azure Data Factory for orchestration
• Familiarity with streaming frameworks (Spark Structured Streaming, Kafka) and/or CDC patterns
• Understanding of data quality frameworks, validation checks, and observability concepts
• Experience integrating Databricks with BI tools such as Power BI, Tableau, or Looker
• Awareness of cost optimization strategies in cloud‑based data platforms

Education

• Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience)

Why This Role

• Hands‑on ownership of Databricks Lakehouse implementations in a real‑world enterprise environment
• Direct client‑facing exposure with a leading Bay Area organization
• Opportunity to evolve from senior data engineering into formal data architecture responsibilities
• Strong growth path toward Senior Databricks Architect / Lead Data Platform Engineer

Seniority Level

• Mid‑Senior level

Employment Type

• Full‑time

Job Function

• Consulting

Industries

• IT Services and IT Consulting
• Hospitals and Health Care
• Biotechnology Research

Benefits

• Medical insurance
• Vision insurance
• 401(k)