Lead Data Engineer - Google Cloud & Azure Databricks (Kansas City)

Burns & McDonnell

Kansas City (MO)

On-site

USD 140,000 - 200,000

Full time

6 days ago

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Job summary

Burns & McDonnell is seeking a senior data engineer who combines an architecture mindset with hands-on execution to design scalable data systems. You will own end-to-end pipelines across Google Cloud and Azure Databricks, mentor engineers, and bridge business needs with robust data solutions.

You will lead technical direction, establish data engineering standards, and collaborate with product managers and analysts to deliver secure, reliable data platforms across multi-cloud environments.

Qualifications

Bachelor’s degree in Computer Science, MIS, Engineering or related field.
8+ years in data engineering or related field.
Strong programming and coding principles.
Excellent communication and collaboration skills.
Experience with multi-cloud data platforms is a plus.
Hands-on production experience with Google Cloud and Azure Databricks.

Responsibilities

Architect, build, and ship scalable data pipelines to production.
Lead end-to-end data product lifecycles and define SLAs.
Provide technical leadership and mentorship to engineers.
Set standards for CI/CD, testing, and documentation.
Drive multi-cloud data movement, security, and cost governance.
Collaborate with product managers and stakeholders to align roadmaps.
Define data schemas and ensure data quality baselines.
Monitor pipelines and troubleshoot production issues.
Evaluate new tech, including generative AI integrations.

Skills

Programming languages

Web development

Communication

Analytical thinking

Team collaboration

Documentation

Certifications

Education

Bachelor degree in CS/MIS/Engineering

Tools

Google Cloud Platform

BigQuery

Dataflow

Azure Databricks

Delta Lake

Unity Catalog

Dataform

dbt

Airflow

Kafka

Pub/Sub

Confluent

Job description

Description

Overview

We're looking for a senior engineer who combines an architecture mindset with deep hands-on experience - someone who can design scalable systems and personally build and ship them into production. As our lead data engineer, you will own pipelines end-to-end across Google Cloud and Azure Databricks, from whiteboard to running code, while setting the technical direction for our enterprise data products, mentoring the team, and serving as the bridge between business stakeholders and engineering to turn strategic goals into robust, secure data solutions.

Responsibilities

Architect, build, and ship scalable batch and streaming data pipelines into production using BigQuery Dataform, Google Cloud Dataflow, Confluent Kafka, and Pub/Sub.
Design and build data pipelines on Azure Databricks using Apache Spark, Delta Lake, and Unity Catalog, keeping engineering patterns consistent with our Google Cloud platform.
Manage the end-to-end data product lifecycle, serving as the technical owner for critical data assets and establishing clear service level objectives.
Provide technical leadership and mentorship to junior and senior engineers, conducting code reviews and fostering a culture of technical excellence.
Lead the engineering team across Google Cloud and Azure Databricks environments, setting multi-cloud architecture standards for data movement, interoperability, security, and cost management.
Partner with product managers, business analysts, and leadership to define data product requirements, align roadmaps, and manage engineering delivery schedules.
Define, document, and enforce data engineering standards, including CI/CD practices, robust testing frameworks, and comprehensive technical documentation.
Own the monitoring, cost optimization, and troubleshooting of production pipelines to maintain reliable business operations.
Evaluate emerging technologies, particularly generative AI integrations and advanced governance tools, to continuously improve data platform capabilities.

Qualifications

Required

Bachelor Degree in Computer Science, Management Information Systems, Engineering or related field and 8 years Required
Expert knowledge in programming languages and coding principles and procedures.
Expert in web development framework.
Strong attention to detail, facilitation, team building, collaboration, organization, and problem-solving skills.
Excellent verbal and written communication skills.
Ability to work methodically and analytically in a quantitative problem-solving environment.
Demonstrated critical thinking skills.
Expert in Microsoft Office Suite (Word, Excel, and PPT).
Strong attention to detail, facilitation, team building and collaboration.
Ability to obtain applicable certifications.

Strongly Preferred

Ten or more years of experience in data engineering, data warehousing, or data architecture, with at least five years in a technical leadership, mentoring, or consulting role.
Deep technical expertise with Google Cloud, BigQuery, Dataflow, and related cloud data services.
Hands-on production experience with Azure Databricks, including Apache Spark (PySpark), Delta Lake, and Unity Catalog.
Proven experience architecting and leading delivery on multi-cloud data platforms, ideally spanning Google Cloud and Azure.
Production experience designing and deploying real-time streaming architectures using messaging platforms like Confluent Kafka or Google Pub/Sub.
Strong expertise in workflow orchestration using scheduling tools like Airflow.
Advanced SQL skills and extensive experience with transformation frameworks like Dataform or dbt.
Production experience with at least one transactional database, such as Oracle, SQL Server, or PostgreSQL.
Strong hands-on coding skills in Python or Java, with a track record of personally shipping production-grade data pipelines.
Demonstrated experience managing data product delivery, defining schemas, and establishing data quality baselines.
Strong communication skills with a proven track record of collaborating with cross-functional stakeholders and translating business needs into technical specifications.
Strong problem-solving abilities and experience leading engineering teams in fast-paced environments.
Experience coding with agentic tooling as part of generative AI workflows.
Direct experience implementing data mesh principles, managing data products, and using tools like Knowledge Catalog (Dataplex) or Entropy Data for contract management.
Advanced knowledge of data security, compliance, and governance tooling such as Immuta and native BigQuery security controls.
Experience operating across hybrid or multi-data platform environments, including Palantir.
Google Cloud Professional Data Engineer or Professional Cloud Architect certification.
Databricks Certified Data Engineer Professional or Microsoft Azure data certifications, plus certifications in supporting technologies such as Power BI.