Title: Data Engineer (Contract) - BigQuery / GA4 Location: Portland, OR (Remote) IT & Digital Contractor Job Description: Duration: ~3 weeks (Approx. 45-55 hours total) Pay Rate: $50-$70/hour on W2 About the Role We're seeking a Data Engineer with strong marketing analytics experience to support a short-term engagement focused on building and validating a BigQuery-based data foundation. This role will work across multiple client accounts to ensure analytics data is properly structured, integrated, and ready for reporting, with a focus on GA4, paid media, and CRM data sources. Key Responsibilities - Set up and configure BigQuery / GCP environments, including datasets, permissions, and naming conventions - Enable and validate GA4 → BigQuery data exports - Ensure key events and conversions are accurately captured and flowing - Connect and validate priority data sources (GA4, paid media platforms, CRM, email tools) - Create reporting-ready tables or views for analytics and performance tracking - Perform data QA and validation across sources (GA4, ad platforms, CRM) - Identify data gaps, inconsistencies, and potential risks - Ensure existing dashboards and reports remain functional - Document data architecture, sources, and workflows - Provide clear handoff documentation and recommendations for future improvements Required Qualifications - Hands-on experience with Google BigQuery and Google Cloud Platform (GCP) - Strong understanding of GA4 (Google Analytics 4) and data exports - Experience working with Google Tag Manager (GTM) - Proficiency in SQL for data transformation and reporting - Experience integrating marketing data sources (paid media, CRM, email platforms) - Strong data validation and QA skills - Ability to work independently in a fast-paced, short-term engagement Preferred Qualifications - Experience in marketing analytics or agency environments - Familiarity with tools like Salesforce, Google Ads, Mailchimp, or similar platforms - Experience creating lightweight reporting layers or data models - Strong documentation and stakeholder communication skills What Success Looks Like - BigQuery environments are fully set up and structured - GA4 data is correctly flowing into BigQuery - Key data sources are connected and validated - Reporting tables/views are available and usable - Data accuracy is confirmed across platforms - Documentation and handoff materials are complete Benefit Offering Benefit Offerings: Benefit offerings include medical, dental, vision, life insurance, short-term disability, additional voluntary benefits, EAP program, and 401K plan. Our program provides employees the flexibility to choose the type of coverage that meets their individual needs. Available paid leave may include Paid Sick Leave, where required by law; any other paid leave required by Federal, State, or local law; and Holiday pay upon meeting eligibility criteria. Equal Opportunity Employer/Veterans/Disabled The Company will consider qualified applicants with arrest and conviction records in accordance with federal, state, and local laws and/or security clearance requirements. Pay Details: $50.00 to $70.00 per hour Search managed by: Sharanya Ram Benefit offerings available for our associates include medical, dental, vision, life insurance, short-term disability, additional voluntary benefits, EAP program, commuter benefits and a 401K plan. Our benefit offerings provide employees the flexibility to choose the type of coverage that meets their individual needs. In addition, our associates may be eligible for paid leave including Paid Sick Leave or any other paid leave required by Federal, State, or local law, as well as Holiday pay where applicable. Equal Opportunity Employer/Veterans/Disabled Military connected talent encouraged to apply The Company will consider qualified applicants with arrest and conviction records in accordance with federal, state, and local laws and/or security clearance requirements, including, as applicable: - The California Fair Chance Act - Los Angeles City Fair Chance Ordinance - Los Angeles County Fair Chance Ordinance for Employers - San Francisco Fair Chance Ordinance Massachusetts Candidates Only: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Role Description We are looking for a Principal Data Engineer to sit at the intersection of data engineering and applied data science. You will own the design, development, and operation of the platforms and pipelines that power our data science capabilities — ensuring data flows reliably from source systems through to analysis, and business consumption. This role is roughly 75% data engineering and 25% data science and is ideal for someone who builds with engineering rigor but thinks with a data science mindset; someone who is energized by building platforms that make AI real in an organization. The right candidate is curious by nature — you explore out-of-the-box ideas and stay current with the fast-moving AI/Machine Learning (ML) landscape. You’ll work directly with: - Business analysts - Product owners - Business end-users - Engineering and application teams - Data/platform engineering teams A consultative communication style is critical as shared outcomes across technology and business are the expectation. Qualifications - Bachelor’s degree in computer science, engineering, mathematics, or a related field, OR 7+ years of equivalent verifiable experience, skillset, and record of accomplishment - Experience in a Principal or Senior Data Engineer role with direct involvement in ML platform or Data Science work - Proficiency in an analytics/BI tool such as Power BI Requirements - Modern data stack technologies — Databricks (strongly preferred), Snowflake, Spark - Inbound/outbound transportation of data with APIs and FTPs - MPP databases such as Databricks, Snowflake, BigQuery, Teradata, or Azure Synapse - Cloud platforms — AWS, Azure, or GCP - Python and SQL - Building and deploying ML models (classification, regression, forecasting, NLP, or similar) - Familiarity with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow - MLflow or similar tools for experiment tracking, model registry, and deployment - Understanding of feature engineering, model evaluation, and common ML failure modes - Strong understanding of data modelling techniques (Kimball, Data Vault) and distributed systems - Familiarity with feature stores, training pipelines, and batch/real-time inference architectures Benefits - 401K company match up to 4% - Paid Time Off - Medical Insurance options including FSA & HSA - Vision Insurance - Dental insurance - Employee Assistance Programs - Team Member Referral Program - Tuition Reimbursement - Wellbeing Program - Career development opportunities

• The Mid-Level Full Stack Data Engineer is responsible for building and supporting data-driven features across backend services and thick client desktop applications. • Develop and maintain data ingestion and transformation pipelines. • Implement backend logic to process, validate, and analyze structured and semi-structured data. • Contribute to RESTful API development and service-layer integrations. • Support integration of processed data into thick client desktop applications (e.g., Electron-based platforms). • Assist in database schema design, data modeling, and query optimization. • Develop UI-integrated data features such as dashboards, validation results, and analytics views. • Write unit and integration tests to validate data processing logic. • Participate in sprint planning, backlog refinement, and demos. • Collaborate with senior engineers and architects to implement scalable and maintainable data solutions.

• Design and implement enterprise data architectures including Data Lakes, Data Warehouses, Data Fabric, and MDM solutions. • Build scalable data pipelines and data integration frameworks. • Define data governance, quality, lineage, and security standards. • Enable AI/ML initiatives through robust data engineering and architecture practices. • Collaborate with business and technology teams to develop data strategies and roadmaps. • Optimize data platforms for performance, scalability, and reliability.