Data Engineer

POSITION SUMMARY:

The Data Engineer is a hands-on technical role focused on full-stack data engineering within the Enterprise Data organization. The role will play a critical part in shaping future big data, analytics, and AI agent engineering initiatives using Snowflake, DBT, SQLDBM, and Medallion architecture. TIMELINE: The first batch of resumes will be sent to the hiring manager on Tuesday, September 29th at 3pm AZ time.

PRINCIPAL RESPONSIBILITIES:

Designs and develops code and data pipelines to ingest from relational databases (Oracle, SQL Server, DB2, Aurora), file shares, and web services. Build Data Lake on AWS S3 with optimal performance considerations by partitioning and compressing data. Data Engineering and Analytics using AWS Glue, Informatica, EMR, Spark, Athena, Python. Designs and builds scalable cloud data warehouses using Snowflake and develops modular, tested transformation pipelines using DBT. Designs and develops code and data pipelines to ingest relational databases, file shares, and web services. Participates in requirements definition, system architecture design, and data architecture design. Participates in all aspects of the software life cycle using Agile development methodologies. Experience designing and implementing Medallion Architecture (Bronze, Silver, Gold layers) for enterprise data platforms. Hands-on experience with SQLDBM for data modeling, schema design, and data warehouse architecture documentation. Experience developing conceptual, logical, and physical data models using SQLDBM and collaborating with business and technical stakeholders on model reviews. Designs and implements Medallion architecture using Bronze, Silver, and Gold layers to support governed, reusable, and analytics-ready data products. Creates and maintains conceptual, logical, and physical data models in SQLDBM, including model documentation and stakeholder design reviews. Engineers AI agents and AI-enabled data solutions that securely use enterprise data, metadata, and analytics capabilities to support business workflows. Applies SQL expertise to data ingestion, transformation, reconciliation, performance optimization, and complex analytical workloads in Snowflake. Implements DBT models, tests, documentation, lineage, source freshness checks, and deployment practices within CI/CD pipelines.

MINIMUM QUALIFICATIONS:

Bachelor's degree in computer science, Computer Information Systems, Engineering, Statistics or closely related field (willing to accept foreign education equivalent) (required). Experience in AWS services for data and analytics (required). 5 years of experience in Data Ingestion, Data Extraction, and Data Integration (required). Hands-on experience with Snowflake, DBT, advanced SQL, SQLDBM, and Medallion architecture (required). Experience designing or integrating AI agents within data engineering or analytics solutions (required).

PREFERRED QUALIFICATIONS:

5+ years of experience in Enterprise Information Solution Architecture, Design, and development required. 5+ years of experience with integration architectures such as SOA, Microservices, ETL or other integration technologies. 5+ years of experience with working content or knowledge management systems, search engines, relational databases, NoSQL databases, ETL tools, geospatial systems, or semantic technology. 5+ years of hands-on experience with AWS services ( S3, Kinesis, Lambda, Athena, Glue, EMR) required. Experience with JSON or XML data modeling required. Experience with Git/GitHub, branching, and other modern source code management methodologies required. Domain knowledge of NoSQL or relational database required. Understanding of database architecture and performance implications required. Experience integrating Business Intelligence applications like PowerBI. Experience with Machine Learning and Artificial Intelligence. Ability to multi-task effectively. Ability to work collaboratively as part of an Agile Team. Extensive knowledge and experience with Python and Snowflake Streamlit. Excellent written and verbal communication skills, sense of ownership, urgency and drive. Experience building governed AI agent solutions using enterprise data, semantic models, vector search, or retrieval-augmented generation patterns. Experience establishing DBT development standards, automated data tests, documentation, lineage, and production deployment controls. Experience designing enterprise data models and warehouse schemas in SQLDBM for Snowflake-based platforms. Apolis