AI And DATA -AI Data Platform Engineer - Snowflake- Senior

Role - AI Data Platform Engineer - Snowflake

Experience Guide - 5-10 years

Primary Skill Area - Cortex AI, Semantic Data, Snowpark & Modern Data Platforms

The opportunity: Build and operate enterprise-grade Data, AI, and Aladdin integration solutions across modern data platforms including Snowflake, Databricks, Microsoft Fabric, and cloud ecosystems. The role focuses on Aladdin inbound and outbound data integration, scalable data engineering, AI-ready data products, data quality and reconciliation, enterprise service integration, platform automation, governance, security, Data SRE, and AI-enabled engineering using modern cloud-native and big data technologies.

• Design, develop, and optimize scalable data pipelines, ingestion frameworks, and integration solutions supporting Aladdin inbound and outbound services across modern data platforms.
• Perform data discovery, asset alignment, source system analysis, data mapping, and reconciliation activities to onboard client data into Aladdin ecosystems.
• Collaborate with client teams, BlackRock stakeholders, architects, and cross-functional engineering teams to support requirements gathering, data parity analysis, technical solutioning, and integration delivery.
• Develop robust batch, streaming, API-driven, event-based, CDC, and file-based ingestion frameworks using modern cloud and big data technologies.
• Build curated, analytics-ready, and AI-ready data products with strong quality controls, lineage, metadata management, and governed consumption patterns.
• Implement and support data packaging, compliance, reporting, and operational workflows aligned with Aladdin integration requirements and onboarding methodologies.
• Establish and execute data quality, validation, audit, reconciliation, and control frameworks to ensure data accuracy, completeness, traceability, and successful parity outcomes.
• Develop reusable platform assets including onboarding accelerators, metadata-driven frameworks, pipeline templates, monitoring solutions, and operational support capabilities.
• Design and manage workflow orchestration, schedulers, dependency management, and cross-platform integrations to support reliable and scalable data processing.
• Optimize Spark, SQL, cloud-native, and distributed processing workloads for performance, scalability, reliability, and cost efficiency.
• Implement Git-based engineering practices, CI/CD pipelines, Infrastructure-as-Code, automated testing, deployment automation, and environment promotion processes.
• Integrate enterprise platforms with APIs, cloud services, governance solutions, security controls, metadata platforms, and downstream analytics and reporting consumers.
• Leverage AI, GenAI, and Agentic AI capabilities for metadata discovery, documentation generation, data quality monitoring, anomaly detection, operational intelligence, and productivity improvements.
• Support implementation of AI-enabled solutions including semantic search, RAG, intelligent data discovery, conversational analytics, and enterprise knowledge retrieval where applicable.
• Implement governance, security, privacy, lineage, audit logging, access controls, and policy-driven data management using enterprise governance and security platforms.
• Build metadata-driven audit, monitoring, observability, and Data SRE capabilities covering pipeline health, data quality, operational metrics, incidents, lineage, usage, performance, and SLA/SLO adherence.
• Support cloud deployment, application registration, platform onboarding, environment management, and operational readiness across Azure, AWS, Microsoft Fabric, Databricks, Snowflake, and related enterprise ecosystems.
• Contribute to technical design reviews, platform standards, engineering best practices, automation initiatives, and continuous improvement of Aladdin integration capabilities.

• Data Platforms & Integration - Databricks, Snowflake, Microsoft Fabric, Spark, Lakehouse architectures, Data Warehouses, Data Lakes, Semantic Data Products, Enterprise Data Integration, Aladdin Data Onboarding and Integration.
• Investment Data & Aladdin - Data Discovery, Asset Alignment, Data Mapping, Data Parity Validation, Reconciliation, Compliance & Reporting Data Flows, Investment Data Management, Aladdin Inbound and Outbound Services.
• AI, GenAI & Agentic Engineering - Cortex AI, Databricks AI/BI & Genie, Mosaic AI, Fabric Copilot, Vector Search, RAG, Graph RAG, Agentic AI, Semantic Retrieval, LLMOps, AI-assisted Data Engineering and Automation.
• Engineering - Python, PySpark, SQL, Snowpark, APIs, Git, CI/CD, dbt, Data Modelling, Unit Testing, Integration Testing, Data Pipeline Testing, GitHub Copilot and Automation Frameworks.
• Cloud & DevOps - Azure, AWS or GCP, Terraform/Open Tofu, Kubernetes, Docker, GitHub Actions, Azure DevOps, Jenkins, Infrastructure-as-Code, Policy-as-Code, Azure Data Factory, Databricks Workflows, Fabric Pipelines, and Enterprise Orchestration Tools.

• 5-10 years of experience in data engineering, data platform operations, analytics engineering, platform engineering, or AI platform enablement.
• Strong hands-on implementation experience with cloud data platforms, APIs, Git connectivity, CI/CD, governed access patterns, SRE practices, and production operations.
• Experience designing and supporting large-scale data integration, onboarding, reconciliation, data quality, compliance, and reporting solutions. Exposure to investment data platforms and BlackRock Aladdin integrations is highly preferred.
• Preferred certifications aligned to the relevant cloud/platform stack, data engineering, DevOps, security, governance, and AI/ML engineering.

• Strong hands-on engineer with architecture awareness, delivery ownership, and a platform engineering mindset.
• Comfortable turning platform standards into reusable frameworks, secure implementation patterns, operational controls, and production-ready services.