Database Developer - Senior

Randstad
Lone Tree, CO, United States
5 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
$121,930.0 - $128,170.0
Working hours
Regular working hours
Job source

Tech stack

Adobe Analytics Testing (Software) Application Programming Interfaces (APIs) Artificial Intelligence Data Analysis JIRA Business Intelligence Development Cloud Database Code Review Continuous Integration Data Infrastructure Data Integration
+30 more
Data Mart Data Mining DevOps Digital Assets Dimensional Modeling Github Python (Programming Language) Microsoft Visual Studio Power BI Search Technologies Software Engineering SQL Databases Data Streaming Tableau (Software) Traffic Analysis Data Logging Data Processing Google Cloud Sql Optimization GitHub Copilot Retrieval-Augmented Generation Delivery Pipeline Large Language Models Build Management Data Analytics Restful APIs Looker Analytics Software Version Control Data Pipelines Api Management

Job description

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., 1. Build AEO Data Marts and Pipelines in Google Cloud Platform - 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 Client 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.

  1. 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.

Requirements

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., Adobe Analytics,APIs,API,AI-enabled,AI,design and build,cloud-based data,code reviews,CI/CD,analytical data,analytics capabilities,integrating data,logging,data marts,data mart,data extraction,data pipelines,building data pipelines,data flows,DevOps practices,data assets,dimensional modeling,GitHub Copilot,GitHub,Google Cloud Platform,Jira,LLM-based,Looker,Visual Studio Code,Power BI,Python,Proficiency in Python,REST APIs,retrieval-augmented generation,SQL,vector search,software engineering,version control,source control,Advanced SQL skills,Tableau,quality testing,traffic data,Communicate,communication skills,issue resolution,Ability to work independently,architecture,AI-assisted development,automation,automated,engineering fundamentals,behavioral data,business intelligence and analytics,business rules,change-management,presenting,demonstrations,API integration,digital marketing,data products,collaborative development,campaign,financial services,marketing,proof of concept,risks,security,Engine Optimization,exception handling,solution architecture,technical documentation,testing,creating documentation,documentation

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