Data Engineer

Role Summary

Wolfe is a Pittsburgh-based FinTech company embedding AI across its product, its internal processes, and the way its teams work day-to-day, and the Data Engineer supports that shift. Working within the Data Platform team, this role builds and maintains the pipelines and data models that turn raw source data into governed, curated datasets the organization can rely on. Day to day it integrates enterprise and marketing sources, including GA4, ad platforms, CRM, email, affiliate, and social alongside core operational systems, into trusted data products, and improves those datasets so teams can use AI-driven and self-service analytics with confidence. The role works closely with senior data engineers under Wolfe's federated hub-and-spoke governance model, taking direction on architecture and standards from the central Data Platform team while independently delivering well-defined data work.

Responsibilities

• Build and maintain ELT/ETL pipelines across the Bronze, Silver, and Gold layers of the lakehouse so data lands reliably and on schedule.
• Integrate enterprise and marketing data sources, including GA4, ad platforms, CRM, email, affiliate, and social, into governed, curated datasets.
• Build dimensional models and semantic data products, following established platform patterns, that business teams can query directly for self-service analytics.
• Implement automated data quality checks, monitoring, and observability across production pipelines, and troubleshoot failures and performance issues.
• Apply data governance standards including cataloging, lineage, tagging, and access controls, in partnership with Data Stewards and senior members of the Data Platform team.
• Prepare clean, well-structured, production-ready datasets that support AI/ML and Agentic AI workflows.

Qualifications

• 2-4 years of experience in data engineering, with hands-on experience building and maintaining production pipelines.
• Strong SQL and working proficiency in Python and/or Spark.
• Experience building and orchestrating pipelines with Airflow, dbt, or similar tools.
• Experience with AWS data services (Glue, S3, Athena, IAM) and cloud data warehouses (Redshift, Snowflake, or BigQuery preferred).
• Working knowledge of dimensional modeling and data governance concepts including cataloging, lineage, and access control.
• Familiarity or interest in AI/ML workflows, with exposure to Agentic AI concepts considered a plus.