EY is looking to solve business problems for F500 clients by building and scaling innovative AI solutions that power strategic growth initiatives and create enterprise value.
Requirements
- Expertise in cloud-native data infrastructure (e.g., GCP/AWS, Snowflake, BigQuery, Databricks, Delta Lake).
- Strong SQL/Python/Scala proficiency and experience with orchestration tools (Airflow, dbt).
- Experience with merging and reconciling third-party data (public APIs, vendor flat files, dashboards).
- Comfort defining semantic layers and mapping unstructured/dirty datasets into usable models for AI/BI use.
- Basic understanding of ML/feature pipelines and downstream modeling needs.
- Experience in data engineering or hybrid data science roles focused on pipeline scalability and schema management.
- Knowledge of how to leverage AI tools in a business setting, including Microsoft Copilot.
Responsibilities
- Lead ingestion and ETL design for structured and semi-structured data (CSV, JSON, APIs, Flat Files).
- Understand schema, data quality, and transformation logic for multiple sources on a client-by-client like NAIC, NOAA, Google Trends, EBRI, Cannex, LIMRA, and internal client logs.
- Design normalization and joining pipelines across vertical domains (insurance + consumer + economic data).
- Build data access layers optimized for ML (feature stores, event streams, vector stores).
- Define and enforce standards for data provenance, quality checks, logging, and version control.
- Partner with AI/ML and Platform teams to ensure data is ML- and privacy-ready (HIPAA, SOC2, etc.).
- Communicate findings and recommendations to stakeholders through compelling data storytelling and visualizations.
Other
- A bachelor’s degree in Business, Statistics, Economics, Mathematics, Engineering, Computer Science, Analytics, or other related field and 8 years of related work experience; or a graduate degree and approximately 7 years of related work experience.
- Proven experience in managing and developing high-performing data science and data engineering teams.
- The ability and willingness to travel and work in excess of standard hours when necessary.
- Collaborative, problem-solving, and growth-oriented mindset.
- Experience working in a startup and/or management/strategy consulting.
