Data Modeler / Data Engineer

Responsibilities

• Design and maintain JSON-based data models that support business workflows and optimize query patterns for both transactional and analytical use cases.
• Lead MongoDB schema evolution, including versioning, backward compatibility, and migration strategies aligned with changing data requirements.
• Apply best practices for indexing, partitioning/sharding, and performance optimization across MongoDB collections.
• Build and maintain data ingestion and curation pipelines using Azure Databricks, Azure Data Factory, and Azure Synapse.
• Develop and optimize PySpark pipelines for scale, performance, and cost efficiency within cloud environments.
• Collaborate with analytics teams and business stakeholders to curate high-quality datasets for downstream reporting and analytics.
• Ensure end-to-end compliance with enterprise data governance standards, including privacy, access controls, metadata management, and audit readiness.

Must-Have Skills

• MongoDB & JSON Data Modeling: Strong hands-on experience designing JSON/XSD/JSON schemas, managing schema evolution, versioning, and migration strategies with a focus on performance and data quality.
• Azure Data Engineering: Advanced SQL and PySpark (Spark SQL, performance tuning) with hands-on experience in Azure Databricks, Azure Data Factory (ADF), and Azure Synapse for ingestion and curation pipelines.
• Governance & Stakeholder Collaboration: Proven ability to work with business and analytics partners, translate requirements into scalable data solutions, and ensure adherence to governance, privacy, and metadata standards.