Top Tech Jobs & Startup Jobs

Lead the design, development, and optimization of scalable batch and real-time data pipelines and platforms. Build cloud-based data solutions using distributed processing, streaming technologies, and modern data warehouses. Define data models, improve performance and reliability, establish engineering best practices, and provide technical guidance to data engineers while collaborating with architects, product teams, developers, and stakeholders.

Lead end-to-end delivery of a Plant Performance Reporting transformation across manufacturing plants, manage executive stakeholders and governance, coordinate cross-functional and offshore teams, prioritize Snowflake data engineering and Power BI productionalization, oversee UAT and deployment, and drive adoption, training, and reporting standardization.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

Lead end-to-end custodial data integrations from Tamarac into Salesforce, troubleshoot data and pipeline issues, and design/optimize Salesforce CRM workflows for wealth management. Serve as a hands-on SME, manage stakeholders, and deliver scalable solutions with minimal oversight.

The role involves managing large-scale data analytics projects, designing data solutions, and collaborating with executives to implement digital and cloud solutions.

As a Senior Data Steward Manager, you'll oversee data management and governance, ensure data quality, and work with cross-functional teams to meet data standards.

The Databricks Architect will design and implement data architecture solutions using Databricks, AWS, and Snowflake, focusing on MLOps and data governance.

Lead enterprise data modernization by designing canonical and semantic data models, data contracts, and real-time streaming architectures (Kafka). Architect AWS and Snowflake-based pipelines, enforce governance and metadata/lineage standards, partner with product and engineering teams, and produce reusable modeling artifacts to enable analytics and AI/ML use cases.

Design and lead the development of cloud-native Generative AI platforms and solutions, ensuring scalability, security, and integration with enterprise applications.

The MLOps Engineer will build, deploy, and monitor machine learning models using AWS services and develop data quality solutions, collaborating with teams.