Data Modeler Architect Consultant

Agama Solutions•Jersey City, NJ

1d•Onsite

About The Position

This role involves extensive enterprise-scale Data Architecture work to serve the ODS and ADS requirements of the business. The ideal candidate will have experience in Data Modeling and building ingestion pipelines for both near real-time and batch processing. Experience with Hadoop data ingestion and Hive Data models is essential. The role requires understanding various technological solutions for building Canonical Data Models (CDM) and big data solutions, including their pros and cons. Key considerations include identifying the best ingestion frameworks for performance, evaluating options for CDM design (flat vs. normalized structures and their implications), and assessing analytics design choices (flat vs. normalized structures and their implications). Understanding performance challenges for each solution is also critical. Experience in Commercial Banking and modeling data platforms is required. The candidate must be hands-on with technologies such as SQL, NoSQL, big data, near real-time messaging (e.g., Kafka), and batch-based ETL.

Requirements

Extensive enterprise-scale Data Architecture experience.
Data Modeling experience.
Experience building ingestion pipelines (near real-time and batch).
Experience with Hadoop data ingestion.
Experience with Hive Data models.
Understanding of technological solutions for Canonical Data Model (CDM) and big data.
Knowledge of ingestion frameworks and performance optimization.
Understanding of CDM design options (flat vs. normalized) and their pros/cons.
Understanding of analytics design options (flat vs. normalized) and their pros/cons.
Awareness of performance challenges in data solutions.
Commercial Banking experience.
Experience modeling data platforms.
Hands-on experience with SQL.
Hands-on experience with NoSQL.
Hands-on experience with big data technologies.
Hands-on experience with near real-time messaging technologies (e.g., Kafka).
Hands-on experience with batch-based ETL technologies.

Nice To Haves

Experience with containerization technologies (Docker, Kubernetes).
Knowledge of machine learning tools and integration with Hadoop.
Experience in migrating on-prem Hadoop clusters to cloud platforms.
Familiarity with CI/CD pipelines for big data solutions.
Product mindset.
Experience formulating product-centric data strategy for a financial services client.
Exposure to treasury areas like liquidity management, payments, or capital management.

Responsibilities

Perform extensive enterprise-scale Data Architecture work.
Build ingestion pipelines for near real-time and batch processing.
Develop and maintain Hadoop data ingestion and Hive Data models.
Evaluate and recommend technological solutions for Canonical Data Models (CDM) and big data solutions.
Analyze and advise on the best ingestion frameworks for performance.
Determine optimal CDM design structures (flat vs. normalized) and their pros/cons.
Determine optimal analytics design structures (flat vs. normalized) and their pros/cons.
Address performance challenges associated with various data solutions.
Model data platforms within the Commercial Banking domain.
Implement and utilize technologies including SQL, NoSQL, big data, near real-time messaging (Kafka), and batch ETL.