Senior Data Engineer (212107)

Aquent, a premier talent solutions partner, is thrilled to collaborate with a leading financial services organization that is at the forefront of leveraging modern data products and AI-enabled analytics. This is an exceptional opportunity to make a significant impact by shaping the future of data intelligence and analytics, directly contributing to innovative solutions that drive strategic business decisions and enhance digital experiences for millions of users. Join a team where your expertise will directly influence the development of cutting-edge data capabilities and contribute to the evolution of intelligent planning and analytics. About the Role We are seeking a highly skilled and passionate Data Engineer to join a dynamic team focused on building robust data foundations for advanced analytics and AI initiatives. In this pivotal contract role, you will be instrumental in designing, developing, and maintaining critical data infrastructure that powers intelligent insights. Your work will directly support the creation of modern data products and AI-enabled capabilities, transforming raw data into actionable intelligence. This is an exciting chance to apply your expertise in a fast-paced environment, contributing to projects that have a tangible impact on business strategy and customer engagement. What You'll Do • Design and build scalable data marts and automated pipelines within a leading cloud platform. • Develop automated pipelines that ingest data from third-party vendor APIs. • Utilize Python to create reusable API integrations, extraction processes, and data-transformation components. • Manage API authentication, pagination, response processing, error handling, and logging for robust data ingestion. • Create well-structured analytical data models that support reporting, in-depth analysis, and future AI use cases. • Write complex SQL from scratch to transform, integrate, validate, and prepare diverse datasets. • Integrate vendor data with relevant digital marketing and core business datasets to provide a holistic view. • Establish appropriate data-quality checks, reconciliation processes, and monitoring to ensure data integrity. • Document source structures, business rules, data grain, refresh frequency, dependencies, and transformation logic. • Partner with internal technology teams to prepare pipelines and data products for production deployment. • Design solutions that are maintainable, reusable, observable, and aligned with enterprise technology standards. • Support downstream consumption of data through business intelligence platforms, analytical workflows, and AI-enabled applications. • Maintain, troubleshoot, and enhance existing digital marketing data marts used for reporting, business intelligence, and analytics. • Support data models containing clickstream and web-traffic data integrated with core business data. • Review, troubleshoot, and optimize complex SQL queries. • Develop reusable datasets that support business intelligence 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 business intelligence 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 • 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. What You'll Bring 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 a leading 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 search analytics, digital visibility, content intelligence, or a related area. • Experience working with third-party marketing or analytics vendor APIs. • Experience with 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 AI-assisted development tools. • Familiarity with collaborative development tools, pull requests, code reviews, CI/CD, and DevOps practices. • Experience using a 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. Please note: This position is available on a W-2 employment basis only and is not open to Corp-to-Corp (C2C) arrangements. The client is also unable to provide employment-based visa sponsorship or transfers for this role.