Data Engineer

American Heritage Credit Union•Philadelphia, PA

About The Position

American Heritage Credit Union, a $5+ billion credit union, has an immediate opening for a Data Engineer. This position is responsible for designing, developing, and maintaining data infrastructure to support efficient data storage, processing, and analysis across American Heritage. Focus on building and optimizing data pipelines while ensuring data systems are scalable, reliable, aligned with business needs, and compliant with organizational guidelines and applicable regulations. Collaborate with data scientists, analysts, and other technical teams to ensure high-quality data delivery, supporting advanced analytics, machine learning, and other initiatives.

Requirements

One to three years of similar or related experience.
Experience with server, networking, and database management.
Experience with database technologies (SQL, NoSQL) and an understanding of data modeling concepts.
Understanding of data governance principles and best practices to ensure data quality and compliance.
Strong familiarity with data integration processes and basic SQL knowledge.
Familiarity with data pipeline tools and frameworks such as Kafka, Airflow, or Spark.
Proficiency in or ability to learn programming or scripting languages such as Python, Java, or similar.
Proficiency with Microsoft Office suite (Outlook, Word, Excel, PowerPoint).
Self-starter with willingness to learn and adapt to new tools, technologies, and methodologies in data engineering.
Project management skills and familiarity with project management tools.
Demonstrated ability to work effectively with cross-functional teams to deliver data solutions that meet business needs.

Nice To Haves

Experience with modern data tools and platforms such as cloud services (AWS, Azure, GCP) or data warehouse solutions like Snowflake or Databricks is a plus but not mandatory.

Responsibilities

Design, build, and maintain scalable ETL (Extract, Transform, Load) pipelines to ingest structured and unstructured data into enterprise data platforms.
Integrate cloud, on-premise, and vendor-managed data sources aligned to business and innovation priorities.
Support the technical enablement of analytics, artificial intelligence (AI), and machine learning (ML) platforms such as Datava and BlastPoint.
Apply cleansing, filtering, enrichment, and standardization processes to ensure usable and trustworthy data.
Support metadata tagging and data mapping for data cataloging and curated dataset efforts.
Optimize performance for data lakehouse and warehouse environments.
Embed data quality checks and automated validations within pipelines.
Align data engineering work with governance policies, standards, and RACI (Responsible, Accountable, Consulted, Informed) project management responsibilities.
Ensure data systems are secure, compliant, and aligned to AI TRiSM (Trust, Risk, Security Management) and other responsible AI frameworks.
Continuously monitor and maintain data systems, ensuring performance optimization and troubleshooting issues as they arise.
Monitor data flows, troubleshoot pipeline issues, and optimize processing routines.
Implement and manage caching, indexing, and other performance-enhancing strategies to support efficient data querying.
Work cross-functionally with analysts, data scientists, governance, and innovation teams.
Support innovation projects involving GenAI (Generative AI), predictive modeling, and member personalization.
Contribute reusable code assets, templates, and notebooks to shared libraries for data science use.