Data Engineer

Role: Data Engineer Location: Lone Tree, CO - Onsite 4 days weekly in Duration: 4+ Months Your Opportunity The Intelligent Planning and Analytics team is seeking a hands‑on Data Engineer to support the development of modern data products and AI‑enabled analytics capabilities. The primary focus of this contract position is to design and build Answer Engine Optimization (AEO) data marts and supporting data pipelines in Google Cloud Platform. The role will also help maintain the team's existing digital marketing data mart supporting business intelligence and analytics. This role is ideal for someone who combines strong data‑engineering fundamentals with practical experience in cloud platforms, SQL, Python and APIs. The successful candidate will be able to build technical solutions while also creating documentation, collaborating with technology partners, and sharing reusable practices with the broader analytics organization. Role Priorities Build AEO Data Marts and Pipelines in GCP - Primary Priority The contractor's primary responsibility will be developing the data foundation for the team's Answer Engine Optimization initiatives. Design and build scalable AEO data marts in Google Cloud Platform. Develop automated pipelines that ingest data from third‑party AEO vendor APIs. Use Python to build reusable API integrations, extraction processes, and data‑transformation components. Manage API authentication, pagination, response processing, error handling, and logging. Create well‑structured analytical data models that support reporting, analysis, and future AI use cases. Write complex SQL from scratch to transform, integrate, validate, and prepare AEO data. Integrate AEO vendor data with relevant digital marketing and core business datasets. Establish appropriate data‑quality checks, reconciliation processes, and monitoring. Document source structures, business rules, data grain, refresh frequency, dependencies, and transformation logic. Partner with Schwab Technology Services and other internal technology teams to prepare AEO pipelines and data products for production. Design solutions that are maintainable, reusable, observable, and aligned with enterprise technology standards. Support downstream consumption of AEO data through business intelligence platforms, analytical workflows, and AI‑enabled applications. Maintain the Digital Marketing Data Mart for BI and Analytics - Second Priority The third priority will be maintaining and enhancing the existing digital marketing data mart used for reporting, business intelligence, and analytics. Maintain, troubleshoot, and enhance the digital marketing data mart. Support data models containing Adobe Analytics clickstream and web‑traffic data integrated with core business data. Write, review, troubleshoot, and optimize complex SQL from scratch. Develop reusable datasets that support BI dashboards, recurring reporting, and ad hoc analytics. Evaluate source data, joins, table grain, business rules, refresh schedules, and downstream dependencies. Monitor data quality and resolve completeness, consistency, performance, and refresh issues. Update data models as reporting and analytical requirements evolve. Apply established standards for table design, column naming, audit fields, retention, and technical documentation. Partner with BI developers and analysts to ensure datasets are understandable, trusted, and fit for purpose. Help improve the maintainability and scalability of existing SQL and data‑processing workflows. Additional Responsibilities Productionization and Technical Documentation Create clear technical documentation for data marts, pipelines, APIs, AI agents, and analytical datasets. Document solution architecture, data flows, source dependencies, business rules, configurations, ownership, and support procedures. Define source expectations such as data keys, grain, schema, refresh cadence, latency, and change‑management considerations. Support code reviews, version control, testing, deployment preparation, monitoring, and issue resolution. Communicate technical risks, dependencies, decisions, and progress to both technical and non‑technical stakeholders. Work collaboratively with technology, security, architecture, and production‑support teams. Required Qualifications Professional experience in data engineering, analytics engineering, software engineering, or a related technical field. Advanced SQL skills, including demonstrated ability to write complex SQL from scratch. Proficiency in Python for API integration, data extraction, automation, and transformation. Hands‑on experience designing and building data pipelines and analytical data models. Experience integrating data from REST APIs or other third‑party interfaces. Hands‑on experience with Google Cloud Platform and cloud‑based data services. Experience building data marts or other curated analytical data products. Knowledge of dimensional modeling, data grain, transformation logic, and reusable data assets. Experience with data‑quality testing, validation, logging, monitoring, and exception handling. Experience using source control and collaborative development practices. Strong technical documentation and communication skills. Ability to work with business, analytics, engineering, architecture, and production‑support partners. Ability to work independently and manage priorities across multiple related initiatives. Preferred Qualifications Experience building data products for Answer Engine Optimization, search analytics, digital visibility, content intelligence, or a related area. Experience working with third‑party marketing or analytics vendor APIs. Experience with Adobe Analytics, clickstream data, web‑traffic data, or other event‑level digital behavioral data. Experience integrating digital marketing data with client, account, product, campaign, or other core business information. Experience developing AI agents from proof of concept into production. Experience with modern analytics‑engineering technologies, including: dbt, dlt, and DuckDB. Working knowledge of modern AI capabilities, including LLM‑based applications, retrieval‑augmented generation, vector search and embeddings, AI tool‑use patterns, agentic AI frameworks and orchestration, model and agent evaluation, and human‑in‑the‑loop controls. Experience with business intelligence and analytics platforms such as Tableau, Looker, Power BI, or similar tools. Experience using GitHub Copilot in Visual Studio Code or comparable AI‑assisted development tools. Familiarity with GitHub, pull requests, code reviews, CI/CD, and DevOps practices. Experience using Jira or a similar work‑management platform. Experience presenting technical concepts, demonstrations, or engineering best practices to other teams. Previous experience in financial services or another highly regulated industry. What Success Looks Like Deliver reliable and well‑documented AEO data marts and automated API pipelines in GCP. Maintain a trusted digital marketing data mart that effectively supports BI, reporting, and analytics. Produce reusable SQL, Python, documentation, and development patterns. Build effective working relationships across analytics and technology teams #J-18808-Ljbffr