Data Engineer

Required experience

• 5+ years of professional data engineering or cloud engineering experience, with at least 2+ years

on Google Cloud.

• Demonstrated production experience with BigQuery

• Strong SQL skills

• Solid understanding of data warehousing concepts including medallion / lakehouse architectures,

dimensional modeling, slowly changing dimensions, and data contracts.

• Working knowledge of data security and governance concepts: IAM, encryption, PII handling, data

classification, and audit logging.

Preferred experience

• Google Cloud Professional Data Engineer certification.

• Hands-on experience with Dataplex (or comparable governance and catalog platforms such as

Collibra, Alation, or Informatica EDC) for cataloging, lineage, and data quality.

• Experience implementing infrastructure-as-code (Terraform) and CI/CD for data platforms.

• Experience integrating data from CDK Global DMS, Reynolds & Reynolds, or similar automotive

dealership management systems.

• Experience working in multi-entity, multi-vertical, or post-acquisition data integration environments.

• Familiarity with Vertex AI, Gemini, or other GenAI tooling, and patterns for governed AI use cases

(synthetic data, DLP-protected sandboxes, RAG).

• Experience with Looker (LookML) or other modern BI semantic layers.

• Exposure to SIEM and log analytics platforms (Google SecOps / Chronicle, Splunk, Microsoft

Sentinel) feeding into or out of the warehouse.

What you will do

Build the data platform

• Design and implement a medallion-architecture (bronze / silver / gold) data warehouse in

BigQuery, including ingestion, transformation, and curated semantic layers.

• Stand up and operate Dataplex for data cataloging, lineage, data quality, and unified governance

across business domains.

• Build batch and streaming ingestion pipelines from sources such as CDK Global DMS, ERPs,

telematics, IoT devices, SaaS APIs, and on-premise databases using tools such as Dataflow,

Pub/Sub, Datastream, Cloud Composer (Airflow), and Cloud Run.

• Develop transformation pipelines using SQL, dbt, or Dataform, with strong attention to modularity,

testing, and version control.

Operate and harden

• Implement infrastructure-as-code for all cloud resources, with CI/CD pipelines for data and

infrastructure deployments.

• Build clear separations for Development / Testing / Production data environments.

• Establish monitoring, alerting, cost controls, and FinOps practices for BigQuery slot usage,

storage tiers, and pipeline reliability.

• Implement security controls including IAM, VPC Service Controls, CMEK, column- and row-level

security, and integration with our identity provider.

• Partner on DLP, masking, and data classification strategies that support both analytics and AI use

cases (including governed sandbox environments).

Enable the business

• Partner with vertical leaders, finance, and operations to translate business questions into

well-modeled, performant data products.

• Build curated marts and semantic models that power BI tools (Looker, Power BI, Tableau, or

similar) and self-service analytics.

• Prepare the platform to serve downstream AI and ML use cases, including feature stores, vector

search (BigQuery, Vertex AI), and Retrieval-Augmented Generation patterns.

• Document architectures, data contracts, and runbooks