Data Architect

Primary focus: Data readiness, canonical standards, conceptual modelling, and gap analysis.

• Lead the creation of the conceptual, logical, and physical data models.

• Lead the Conceptual Data Model (CDM) development for entities relevant to client’s models (Customer, Account, Transaction, Product).

• Conduct data profiling and quality assessments of required model inputs.

• Map current attributes to client’s model feature requirements and identify gaps.’

• Assess readiness of data domains (transaction history, merchant detail, behavioral metrics).

• Review existing canonical data standards, metadata, and lineage to determine compatibility with modelling needs.

• Define early-stage transformation and enrichment needs (behavioral features, aggregations, rolling windows).

• 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.

• Evaluate feasibility of creating model-ready datasets.

DataArchitect Data Modeling Tool, Continuous Improvement, Technical Support, Customer Experience, Customer Support/Service, Exceeded Sales Goal, DataArchitect Data Modeling Tool, Data Modeling, Data Profiling, Metrics, * 10+ years of PHP development experience, including API design and integration work.

• Proven experience integrating with credit card processors or servicers (e.g., TSYS, Fiserv, etc.).

• Strong understanding of banking systems and core banking integration patterns.

• 7-10+ 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 modelling, ingestion patterns, and performance scaling.

• Hands-on experience designing data architectures to support ML workloads, feature engineering and model scoring.

• Demonstrated ability to design end-to-end pipelines in complex, regulated data environments.

• Strong understanding of Azure cloud platform, IAM, and enterprise security practices.

• Excellent communication skills with ability to collaborate across engineering, data science, product, and governance teams.

• Prior involvement in ML Ops architectures or model deployment frameworks.

Compensation: $150,000.00 per year

About US: We are a company that provides innovative, transformative IT services and solutions. We are passionate about helping our clients achieve their goals and exceed their expectations. We strive to provide the best possible experience for our clients and employees. We are committed to continuous improvement and innovation, and we are always looking for ways to improve our services and solutions. We believe in working collaboratively with our clients and employees to achieve success.