Google Cloud Data Engineer

Guidehouse•Tysons Corner, VA

About The Position

Guidehouse is seeking an experienced Data Engineer to join our Technology AI and Data practice within the Defense & Security segment. This individual will have a strong data engineering background and be a hands-on technical contributor, responsible for designing, implementing, and maintaining scalable, cloud-native data pipelines which power interactive dashboards that enable federal clients to achieve mission outcomes, operational efficiency, and digital transformation. This is an exciting opportunity for someone who thrives at the intersection of data engineering, Google Cloud technologies, and public sector modernization. The Data Engineer will collaborate with cross-functional teams and client stakeholders to modernize legacy environments, implement scalable BigQuery-centric data pipelines using Dataform and Python, and support advanced analytics initiatives for our federal client within the insurance space. Client Leadership & Delivery Collaborate with government clients to understand enterprise data architecture, ingestion, transformation, and reporting requirements within a Google Cloud Platform (GCP) environment. Communicate technical designs, tradeoffs, and delivery timelines clearly to both technical and non-technical audiences. Lead the development of extract-transform-load (ETL) and extract-load-transform (ELT) pipelines using Cloud Composer (GCP hosted Airflow), Dataform, and BigQuery to support our analytical data warehouse powering downstream Looker dashboards. Adhere to high-quality delivery standards and promote measurable outcomes across data migration and visualization efforts. Solution Development & Innovation Design, develop, and maintain scalable ETL/ELT pipelines using SQL (BigQuery), Dataform (SQLX), Cloud Storage, and Python (Cloud Composer/Airflow, Cloud Functions). Apply modern ELT/ETL and analytics engineering practices using BigQuery and Dataform to enable version-controlled, testable, and maintainable data transformations. Leverage tools such as Gitlab and Github to manage version control, merge requests, and promotion pipelines. Optimize data pipelines and warehouse performance for large-scale analytical workloads, including partitioning, clustering, incremental processing, and cost optimization to enable downstream BI utilizing Looker. Validate compliance with federal data governance, security, and performance standards. Design and document enterprise data models, metadata strategies, data lineage frameworks, and other relevant documentation, as needed. Align data from multiple discrete datasets into a cohesive, interoperable architecture, identifying opportunities for linkages between datasets, normalization, field standardization, etc. Assist with cleanup of existing data and models, including use of ETL. Practice & Team Leadership Work closely with data architects, data scientists, data analysts, and cloud engineers to deliver integrated solutions. Collaborate across Scaled Agile Framework (SAFe) teams and participate in Agile ceremonies including standups, retrospectives, and Program Increment (PI) planning. Manage tasks and consistently document progress and outcomes using Confluence and Jira. Support documentation, testing, and deployment of data products. Mentor junior team members and contribute to reusable frameworks and accelerators. Contribute to thought leadership, business development, and best practice development across the AI & Data team.

Requirements

US Citizenship and the ability to obtain and maintain a federal Public Trust clearance. Individuals with an active Public Trust clearance are preferred.
Bachelor’s degree in computer science, engineering, mathematics, statistics, or a related technical field.
Minimum One (1) to Five (5) years of experience in data engineering within cloud environments.
Strong proficiency in SQL for data modeling and data quality tests and Python for pipeline design.
Comfortable with the command line in Linux for git, deploying code to the cloud, and interacting with cloud files.
Experience with orchestration tools including but not limited to Cloud Composer (Airflow), Luigi, Prefect, Dagster, etc.
Experience with modern analytics engineering toolsets (dbt, Dataform, Databricks, etc.) and familiarity with best practices and methodologies behind the tools (data lineage, dependency graphs, tags, data quality tests, etc.).
Proven experience with business intelligence tools and cloud platforms (AWS, Azure, GCP).
Familiarity with CI/CD practices, principles, and tools such as Gitlab, separation of environments, and idempotency as it relates to data pipelines.
Experience building ETL/ELT pipelines and integrating data sources into reporting platforms.
Familiarity with data governance, metadata, and compliance frameworks.
Excellent communication, facilitation, and stakeholder engagement skills.

Nice To Haves

Master’s degree in computer science, engineering, mathematics, statistics, or a related technical field.
Experience in data engineering within cloud environments.
Familiarity with machine learning (ML) and optimal data configurations to support model workloads.
Familiarity with Agile project management methodologies and Atlassian toolsets (Confluence, Jira).
Understanding of data quality and data pipeline test procedures and best practices.
Experience working within matrixed teams of data engineers, BI developers, and QA engineers.
Experience implementing cloud data governance and data management tools such as Dataplex.
Experience with serverless architectures including cloud functions.
Experience with Javascript to support development in Dataform.
Experience using LLM-based coding assistants such as Gemini Code Assist to automate and streamline software development tasks.
Relevant GCP certifications such as GCP Professional Data Engineer, GCP Professional Cloud Architect, and GCP Professional ML Engineer.
Experience working with public sector clients.
Familiarity with federal contracting and procurement processes.

Responsibilities

Collaborate with government clients to understand enterprise data architecture, ingestion, transformation, and reporting requirements within a Google Cloud Platform (GCP) environment.
Communicate technical designs, tradeoffs, and delivery timelines clearly to both technical and non-technical audiences.
Lead the development of extract-transform-load (ETL) and extract-load-transform (ELT) pipelines using Cloud Composer (GCP hosted Airflow), Dataform, and BigQuery to support our analytical data warehouse powering downstream Looker dashboards.
Adhere to high-quality delivery standards and promote measurable outcomes across data migration and visualization efforts.
Design, develop, and maintain scalable ETL/ELT pipelines using SQL (BigQuery), Dataform (SQLX), Cloud Storage, and Python (Cloud Composer/Airflow, Cloud Functions).
Apply modern ELT/ETL and analytics engineering practices using BigQuery and Dataform to enable version-controlled, testable, and maintainable data transformations.
Leverage tools such as Gitlab and Github to manage version control, merge requests, and promotion pipelines.
Optimize data pipelines and warehouse performance for large-scale analytical workloads, including partitioning, clustering, incremental processing, and cost optimization to enable downstream BI utilizing Looker.
Validate compliance with federal data governance, security, and performance standards.
Design and document enterprise data models, metadata strategies, data lineage frameworks, and other relevant documentation, as needed.
Align data from multiple discrete datasets into a cohesive, interoperable architecture, identifying opportunities for linkages between datasets, normalization, field standardization, etc.
Assist with cleanup of existing data and models, including use of ETL.
Work closely with data architects, data scientists, data analysts, and cloud engineers to deliver integrated solutions.
Collaborate across Scaled Agile Framework (SAFe) teams and participate in Agile ceremonies including standups, retrospectives, and Program Increment (PI) planning.
Manage tasks and consistently document progress and outcomes using Confluence and Jira.
Support documentation, testing, and deployment of data products.
Mentor junior team members and contribute to reusable frameworks and accelerators.
Contribute to thought leadership, business development, and best practice development across the AI & Data team.

Benefits

Medical, Rx, Dental & Vision Insurance
Personal and Family Sick Time & Company Paid Holidays
Position may be eligible for a discretionary variable incentive bonus
Parental Leave and Adoption Assistance
401(k) Retirement Plan
Basic Life & Supplemental Life
Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts
Short-Term & Long-Term Disability
Student Loan PayDown
Tuition Reimbursement, Personal Development & Learning Opportunities
Skills Development & Certifications
Employee Referral Program
Corporate Sponsored Events & Community Outreach
Emergency Back-Up Childcare Program
Mobility Stipend