About the role
The Senior Data Modeller/Engineer will support Enterprise Payments’ operational data store by combining advanced JSON/MongoDB data modeling with cloud-based data engineering on the Rahona platform. The role is split 50/50 between data modeling and data engineering, working closely with data analytics teams and Payments partners. Success in this role means accelerating Payments’ ability to onboard new data pipelines, improve data quality, and comply with governance standards. Although responsibilities are divided between data modelling and data engineering, workload allocation varies based on project priorities. Some projects will require ingestion into the operational data store, some will require only ingestion and curation into the data analytics Rahona platform, and some will require both.
Role Summary and Responsibilities For MongoDB Data Modeller: Design JSON schemas that support business workflows and query patterns. Lead schema evolution and manage versioning/migration strategies. Ensure MongoDB collections follow best practices for indexing, partitioning, and performance. Collaborate with BSAs to understand the data requirements and how those translate into schemas that can be used for both transactional and analytic purposes. For Rahona Data Engineer: Build and maintain ingestion and curation pipelines in Databricks, ADF, and Synapse. Optimize PySpark pipelines for scale, performance, and cost efficiency. Collaborate with client Analytics teams to curate datasets and support downstream analytics. Ensure adherence to client governance (PIA, DAC, metadata standards) during ingestion and curation.
REQUIRED SKILLS:
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MongoDB data modeller (JSON data modelling and schema creation) • 5+ years of experience in JSON-based data modelling. • Lead schema design to ensure data quality and performance. • Create XSD and JSON schemas. • Lead MongoDB schema evolution, versioning, and migration.
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Rahona Data Engineer (data analytics ingestion and curation) • Advanced proficiency with SQL query language. • Advanced proficiency in PySpark, including Spark SQL and performance optimization. • Minimum 2+ years of experience with Azure Cloud Development such as Azure Databricks (primary platform for PySpark), Azure Data Factory (ADF), Azure Synapse • Strong knowledge of data ingestion, ETL processes, and orchestration frameworks for cloud environments. Ability to design and manage data pipelines—extracting, transforming, and loading data—especially in cloud environments.
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Soft Skills • Ability to work with business partners to gather data requirements. • Strong analytical thinking, problem-solving, and troubleshooting abilities. • Self-motivated and proactive, with a keen eye for identifying opportunities for improvement and efficiency. • Excellent communication and documentation skills.
Similar Jobs
About the role
The Senior Data Modeller/Engineer will support Enterprise Payments’ operational data store by combining advanced JSON/MongoDB data modeling with cloud-based data engineering on the Rahona platform. The role is split 50/50 between data modeling and data engineering, working closely with data analytics teams and Payments partners. Success in this role means accelerating Payments’ ability to onboard new data pipelines, improve data quality, and comply with governance standards. Although responsibilities are divided between data modelling and data engineering, workload allocation varies based on project priorities. Some projects will require ingestion into the operational data store, some will require only ingestion and curation into the data analytics Rahona platform, and some will require both.
Role Summary and Responsibilities For MongoDB Data Modeller: Design JSON schemas that support business workflows and query patterns. Lead schema evolution and manage versioning/migration strategies. Ensure MongoDB collections follow best practices for indexing, partitioning, and performance. Collaborate with BSAs to understand the data requirements and how those translate into schemas that can be used for both transactional and analytic purposes. For Rahona Data Engineer: Build and maintain ingestion and curation pipelines in Databricks, ADF, and Synapse. Optimize PySpark pipelines for scale, performance, and cost efficiency. Collaborate with client Analytics teams to curate datasets and support downstream analytics. Ensure adherence to client governance (PIA, DAC, metadata standards) during ingestion and curation.
REQUIRED SKILLS:
-
MongoDB data modeller (JSON data modelling and schema creation) • 5+ years of experience in JSON-based data modelling. • Lead schema design to ensure data quality and performance. • Create XSD and JSON schemas. • Lead MongoDB schema evolution, versioning, and migration.
-
Rahona Data Engineer (data analytics ingestion and curation) • Advanced proficiency with SQL query language. • Advanced proficiency in PySpark, including Spark SQL and performance optimization. • Minimum 2+ years of experience with Azure Cloud Development such as Azure Databricks (primary platform for PySpark), Azure Data Factory (ADF), Azure Synapse • Strong knowledge of data ingestion, ETL processes, and orchestration frameworks for cloud environments. Ability to design and manage data pipelines—extracting, transforming, and loading data—especially in cloud environments.
-
Soft Skills • Ability to work with business partners to gather data requirements. • Strong analytical thinking, problem-solving, and troubleshooting abilities. • Self-motivated and proactive, with a keen eye for identifying opportunities for improvement and efficiency. • Excellent communication and documentation skills.