Data Engineer, AI & Analytics

Jobgether; United States; On-site; USD 100,000 - 150,000; Full time

Benefits offered by this job

Flexible work environment

Professional development opportunities

Access to modern AI and data tools

Collaborative, cross-functional teams

Job summary

Jobgether is seeking an experienced Data Engineer, AI & Analytics in the United States to own end-to-end data pipelines, models, and data products powering analytics and AI initiatives.

You will build scalable data foundations across advertising and commerce platforms, ensuring reliability and accuracy for BI and AI applications, with a focus on AI-ready infrastructure and cross-functional collaboration.

Qualifications

• 3+ years of experience in data engineering or analytics engineering, including ownership of a production dbt project.
• Advanced proficiency in Python and SQL, with production-grade data pipelines.
• Deep dbt expertise: incremental strategies, full-refresh trade-offs, tests, sources, and materializations.
• Strong Snowflake expertise and cloud data ecosystem knowledge.
• Experience with multi-tenant data models and architecture decisions for shared vs. client-specific logic.

Responsibilities

• Design, build, and maintain core data foundations including ingestion pipelines, data models, semantic layers, and data marts for multiple clients.
• Own end-to-end data workflows from raw ingestion to production serving layers with reliable, scalable data products.
• Develop resilient ingestion pipelines handling API changes, rate limits, and retroactive restatements without compromising data quality.
• Create unified models across marketing platforms (Meta, Google, TikTok, Amazon, LinkedIn) and client systems (Shopify, GA4, CRM).
• Develop client-specific models and data marts while discerning when to share logic across the foundation.
• Build semantic layers and standardized metrics for reliable analytics and AI-generated SQL results.
• Leverage AI-agentic development tools to accelerate coding, debugging, and delivery; document patterns for reuse.
• Collaborate with product, engineering, client services, and analytics teams to translate requirements into scalable data solutions.
• Monitor data quality, improve pipeline reliability, and optimize warehouse costs across multi-client environments.
• Deliver production-ready datasets and pipelines enabling AI features, analytics, and client-facing solutions.
• Establish automated testing, reconciliation, monitoring, and quality controls for reliable data products.
• Continuously refine data products based on live feedback and evolving business needs.

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer, AI & Analytics based in United States.

This role sits at the heart of a data organization responsible for the pipelines, models, and data products powering analytics, client delivery, and AI initiatives.

You will own the data lifecycle end to end, from raw ingestion through semantic and serving layers, across a complex multi-client environment.

The work involves solving challenging data engineering problems created by fragmented marketing platforms, inconsistent schemas, attribution logic, currencies, and time zones.

You will build scalable foundations across advertising and commerce data while ensuring reliability, accuracy, and usability for both BI and AI applications.

AI-agentic development will be a core part of the engineering workflow, helping accelerate coding, debugging, architecture, and delivery.

You will collaborate closely with product, engineering, client, analytics, tracking, and data operations teams to turn evolving requirements into production-ready solutions.

This is an opportunity for an experienced data engineer to shape AI-ready infrastructure while working on high-impact problems across a diverse portfolio of clients and technologies.

Accountabilities

• Design, build, and maintain the core data foundation, including ingestion pipelines, data models, semantic layers, and data marts supporting agency, client, internal, and AI consumers.
• Own the full workflow from raw platform data through production serving layers, ensuring reliable, scalable, and well-governed data products.
• Build resilient ingestion pipelines capable of handling API changes, deprecated fields, rate limits, retroactive conversion restatements, and other real-world platform behaviors without compromising downstream data quality.
• Develop unified models across marketing and advertising platforms such as Meta, Google, TikTok, Amazon, LinkedIn, and Microsoft, as well as customer-level integrations with Shopify, Klaviyo, GA4, and client CRM systems.
• Develop and maintain client-specific models, overrides, and bespoke data marts while determining when new logic should remain client-specific versus becoming part of the shared data foundation.
• Build semantic layers and standardized metric definitions that enable reliable analytics and consistent AI-generated SQL results.
• Use AI-agentic development workflows and AI coding tools to accelerate engineering, debugging, architecture, and delivery while documenting successful patterns for broader team adoption.
• Collaborate with product and engineering teams, AI and innovation teams, client-facing teams, and other stakeholders to translate requirements into scalable data solutions.
• Monitor data quality, investigate issues, improve pipeline reliability, and optimize warehouse performance and infrastructure costs across a multi-client environment.
• Deliver production-ready datasets and pipelines that enable AI features, product capabilities, analytics, and client-facing solutions.
• Establish and maintain automated testing, reconciliation, monitoring, and quality controls to support reliable data products.
• Continuously refine data products based on live feedback, usage patterns, client requirements, and evolving business needs.
• Contribute to measurable improvements in development velocity, data reliability, client request turnaround, and adoption of production data assets.

Requirements

• 3+ years of experience in data engineering or analytics engineering, including at least 1 year owning a meaningful production dbt project rather than simply contributing models.
• Advanced proficiency in Python and SQL, with experience writing production-grade code for data pipelines, transformations, and modeling.
• Deep expertise in dbt, including incremental strategies, full-refresh trade-offs, Jinja, macros, packages, testing, snapshots, source freshness, exposures, DAG management, and materialization strategies.
• Strong command of Snowflake and the surrounding cloud data ecosystem, with the ability to operate independently as a foundational data owner.
• Experience designing and maintaining data models in multi-tenant environments and making sound architectural decisions about shared versus client-specific logic.
• Working knowledge of marketing and advertising datasets, including UTMs, attribution windows, platform-reported conversions, warehouse-reported conversions, and related measurement challenges.
• Proven experience managing end-to-end data lifecycles from ingestion through serving, with reliability suitable for both BI and AI applications.
• Familiarity with cloud-native infrastructure, particularly GCP, and infrastructure-as-code principles.
• Demonstrated use of AI-agentic development workflows and tools such as Cursor, Claude Code, or GitHub Copilot for production coding, debugging, and system architecture.
• Experience designing AI-ready data models, including clean semantic layers, feature-oriented datasets, and other foundations for downstream AI initiatives.
• Strong understanding of Git, CI/CD, automated testing, and software engineering best practices for reliable production systems.
• Ability to work iteratively, incorporate live feedback, and continuously improve data products and engineering processes.
• Strong analytical thinking, problem-solving, communication, and cross-functional collaboration skills.
• Advanced spoken and written English proficiency.
• Helpful but not required: experience in an agency, consultancy, services, or multi-client environment.
• Helpful but not required: experience with incrementality, media mix modeling, attribution, server-side tagging, retail data, or marketplace data.
• Interest or experience working closer to the business decisions enabled by data, owning full systems rather than only the modeling layer, and using AI coding agents as a regular part of engineering workflows.

Benefits

• Opportunity to work on AI-ready data infrastructure supporting analytics, client solutions, products, and AI initiatives.
• Exposure to complex marketing, advertising, ecommerce, and customer data across a diverse portfolio of clients.
• AI-native engineering environment where tools such as coding agents are integrated into everyday development workflows.
• Opportunity to influence shared data architecture, semantic standards, automation, and production engineering practices.
• Flexible work environment designed to support distributed collaboration.
• Competitive compensation and benefits package, with specific compensation details determined based on location, experience, skills, and role requirements.
• Professional development opportunities, including support for conferences, courses, books, and other learning initiatives.
• Access to modern AI, data, analytics, and engineering technologies.
• Collaborative, people-focused environment emphasizing curiosity, accountability, technical excellence, and meaningful business impact.
• Opportunities to work closely with Product, Engineering, AI, Client Services, BI, Data Operations, and other cross-functional teams.
• Opportunity to contribute to measurable improvements in engineering velocity, data quality, reliability, and AI adoption.