Description

You will own the design and implementation of data architecture for ML workloads.

Responsibilities

• Lead the creation of conceptual, logical, and physical data models, focusing on CDM development for entities like Customer, Account, and Transaction.
• Conduct data profiling and quality assessments, mapping current attributes to model feature requirements and identifying data gaps.
• Define and oversee the strategy for data ingestion, movement, and synchronization from Snowflake to Databricks.
• Ensure schema consistency, data completeness, and performance for high-volume transactional datasets.
• Partner with engineering and platform teams to ensure the data foundation meets model requirements.

Required Skills

• 10+ years of experience in data architecture, data platform engineering, or large-scale analytics systems.
• Deep expertise with Databricks, PySpark/Spark, Delta Lake, and distributed compute architectures.
• Strong knowledge of Snowflake, including data modeling, ingestion patterns, and performance scaling.
• Hands-on experience designing data architectures supporting ML workloads, feature engineering, and model scoring.
• Demonstrated ability to design end-to-end pipelines in complex, regulated data environments.
• Proven experience integrating with payment processors or services (e.g., TSYS, Fiserv).
• Strong understanding of banking systems and core banking integration patterns.
• Strong understanding of Azure cloud platform, IAM, and enterprise security practices.
• Excellent communication skills across engineering, data science, product, and governance teams.

Key Skills

Education

Any Gradute