Data Engineer – AI, Analytics

This is a fully remote position, open to applicants in Brazil, +1 more country.

📋 Description

• Design, construct, and sustain the foundational data framework, which encompasses ingestion, modeling, and data marts.

• Develop ingestion processes that are resilient to API modifications, deprecated fields, rate limitations, and retroactive conversion restatements.

• Model and reconcile expenditures, impressions, conversions, and revenue across platforms such as Meta, Google, TikTok, Amazon, LinkedIn, and Microsoft.

• Establish customer-level joins across platforms including Shopify, Klaviyo, GA4, and client CRM systems.

• Contribute to customized modeling for clients, implementing bespoke logic, overrides, and client-specific data marts.

• Create semantic layers and metric definitions to ensure consistent AI-generated SQL responses.

• Employ AI-agentic workflows and coding tools to expedite development and establish intelligent data infrastructure.

• Document effective AI-agentic development methodologies for team-wide implementation.

• Collaborate with teams in nova product and engineering, AI/innovation, and client relations.

• Monitor and address data quality concerns.

• Optimize data pipelines for cost efficiency and performance across a multi-client data warehouse.

• Deliver production-ready datasets and pipelines that support AI initiatives, product development, and client teams.

• Construct unified, AI-ready data foundations and enable AI functionalities.

⛳️ Requirements

• Advanced proficiency in both spoken and written English is mandatory.

• A minimum of 3 years of experience in data or analytics engineering.

• At least 1 year of experience managing a significant dbt project in a production environment.

• Advanced skills in Python and SQL, with an emphasis on production-quality code for data pipelines and modeling.

• In-depth knowledge of dbt, including incremental strategies, full-refresh trade-offs, Jinja, macros, packages, generic and singular tests, snapshots, source freshness, exposures, DAG management, and materializations.

• Strong understanding of Snowflake and the associated cloud data ecosystem.

• Experience in modeling within a multi-tenant setting.

• Working familiarity with marketing and advertising datasets, including UTMs, attribution windows, and discrepancies between platform-reported and warehouse-reported conversions.

• Proven track record in designing and managing comprehensive data lifecycles from ingestion through to serving.

• Familiar with cloud-native infrastructure (GCP) and principles of infrastructure-as-code.

• Practical experience with AI-agentic development workflows, such as Cursor, Claude Code, or GitHub Copilot.

• Demonstrated capability in architecting AI-ready data models, including feature stores and clean semantic layers.

• Experience using Git and adhering to CI/CD best practices, including automated testing.

• Comfortable with iterative shipping and refining of data products based on real-time feedback.

🏝️ Benefits

• Equal Opportunity Employer.

• Power Digital does NOT impose any application, processing, or training fees at any stage of the recruitment or hiring process.