Managing Consultant, Databricks Engineer - Richmond

Thought Logic Consulting

$120K — $145K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 7+ years of data engineering or analytics experience, including 2 years with Databricks engineering.
  • Hands-on expertise in Databricks, Delta Lake, and Lakehouse technologies.
  • Strong skills in Apache Spark, PySpark, SQL, and Python for pipeline development.
  • Experience with cloud data platforms such as Snowflake, Redshift, or BigQuery.
  • Proven ability to communicate and solve problems with both technical and non-technical stakeholders.

Responsibilities

  • Design scalable data engineering solutions using Databricks Lakehouse architecture.
  • Develop and optimize various types of data pipelines with Auto Loader and Apache Spark.
  • Build ETL/ELT processes and orchestration workflows using Databricks Jobs and Airflow.
  • Implement data quality and governance practices across data platforms.
  • Establish CI/CD and DevOps practices for data engineering.

Benefits

  • Collaborative team environment focused on problem-solving and client impact.
  • Work alongside experienced consultants and engineers on complex projects.
  • Opportunity to develop client-facing consulting skills.
  • Exposure to a variety of modern data technologies and practices.
Full Job Description
Managing Consultant, Databricks Engineer - Richmond

Department: Data Analytics

Employment Type: Full Time

Location: Richmond

Description

Managing Consultant, Databricks Engineer - Richmond

***Candidates must currently reside in or live within a commutable distance to the Richmond area ****

The Role

We are looking for a technically skilled and motivated Databricks Engineer with 7+ years of experience and strong hands-on Databricks expertise to join our growing Data & Analytics team.

In this role, you'll design and build modern data solutions for clients, with a focus on Databricks Lakehouse architecture, scalable data pipelines, cloud data platforms, data quality, and emerging AI-enabled engineering practices. You'll work alongside experienced architects and consultants while taking ownership of technical delivery and developing your client-facing and consulting skills.

What You'll Do
  • Design and build scalable data engineering solutions using Databricks Lakehouse, including Delta Lake, Unity Catalog, Delta Live Tables/Lakeflow Declarative Pipelines, Delta Sharing, and Uniform (Iceberg) where appropriate.
  • Develop and optimize batch, micro-batch, and streaming data pipelines using Auto Loader, Apache Spark, PySpark, SQL, and Python.
  • Build robust ETL/ELT processes, data models, and orchestration workflows using Databricks Jobs/Workflows, Airflow, dbt, and modern data engineering patterns.
  • Implement data quality, observability, governance, auditability, and performance optimization capabilities across enterprise data platforms.
  • Establish modern CI/CD and DevOps practices for data engineering, including Databricks Asset Bundles, automated testing, deployment automation, and Infrastructure as Code with tools such as Terraform.


Who You'll Work With
  • Experienced consultants, architects, and engineers focused on solving complex business and technology challenges through data.
  • Clients across industries looking to modernize data platforms, improve data accessibility and quality, and create greater value from their data.
  • Cross-functional stakeholders across technology, analytics, business operations, and leadership, requiring both technical depth and strong communication.
  • A collaborative team that values curiosity, humility, technical excellence, hands-on problem-solving, and client impact.


What You'll Bring

What You'll Bring
  • 7+ years of data engineering, analytics, or related technical experience, including at least 2 years of hands-on Databricks engineering and strong experience with Lakehouse technologies.
  • Strong hands-on skills with Databricks, Delta Lake, Unity Catalog, Lakeflow/Delta Live Tables, Apache Spark, PySpark, SQL, and Python, including scalable batch and streaming pipelines.
  • Experience with Databricks Workflows/Jobs, Auto Loader, Airflow, dbt, and/or similar orchestration and transformation technologies, plus experience with cloud data platforms such as Snowflake, Redshift, or BigQuery.
  • Understanding of enterprise data quality, governance, observability, auditability, performance optimization, CI/CD, and automated testing, with exposure to Databricks Asset Bundles and Terraform.
  • Strong consulting, communication, and problem-solving skills, with the ability to work directly with technical and non-technical stakeholders and translate business requirements into practical data solutions.


Bonus Points if You Have
  • Databricks Data Engineer Associate or Professional certification and multiple Databricks project delivery experiences.
  • Experience with modern data and cloud technologies such as Snowflake, Redshift, BigQuery, Kafka, Amazon EMR, Docker, Kubernetes, or Terraform.
  • Experience implementing enterprise data quality and governance using Great Expectations, Collibra, dbt, or Databricks-native capabilities.
  • Exposure to agentic AI and AI-powered development tools, including LangGraph, autonomous agents, GitHub Copilot, Claude Code, Cursor, Windsurf, Codex, or similar technologies.
  • Previous consulting or client-facing technical delivery experience, along with cloud or additional data engineering certifications.

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