Top Tech Jobs & Startup Jobs

Lead end-to-end data migration initiatives across cloud data platforms, including architecture decisions, dependency mapping, cutover planning, validation, risk mitigation, and stakeholder alignment. Build and optimize batch and streaming data pipelines using Databricks, Python, and Spark. Maintain migration documentation, testing strategies, rollback plans, and operational procedures while promoting data governance, quality, and delivery best practices.

Build and deploy LLM-based agents for banking clients using LangGraph, LiteLLM, and Langfuse. Develop backend services, API integrations, guardrails, evaluations, observability, and production infrastructure on AWS. Collaborate with security, networking, DevSecOps, and business teams to resolve delivery issues. Create documentation and runbooks, transfer ownership to client teams, and improve reusable agent templates, deployment pipelines, and platform observability after each embedded engagement.

Industrialize, deploy, monitor, and scale machine learning models in production. Build training, inference, retraining, CI/CD, model tracking, and model-serving pipelines. Use MLflow for versioning and governance, Databricks for workflow orchestration, Docker and Kubernetes for deployment, and observability practices for reliable ML operations. Collaborate with data scientists, data engineers, and business stakeholders to deliver scalable, production-grade ML systems.

Migrate Power BI dashboard data sources to Databricks Datalake views, validate data integrity and business-critical metrics, and prevent volumetric discrepancies. Design optimized DAX queries and complex business logic while collaborating with technical support and dashboard design teams. The role requires advanced Power BI, SQL, Databricks, Unity Catalog, and data validation expertise, along with autonomy in deadline-driven migration projects.

Lead technical direction for a platform team building modern backend and data-stack solutions for ML products. Provide technical leadership, evaluate architectures, unblock teams (backend or data stack), use AI tools in workflows, document decisions via ADRs/RFCs, and support modernization with legacy/on-prem constraints.

Design and build scalable Java backend services for reporting, dashboards, and analytics. Contribute to architecture and technical documentation, collaborate with Product and Design, implement testing and monitoring, ensure production reliability, and mentor engineers while improving backend development practices.

Industrialize, deploy, monitor, and scale machine learning models in production. Design training, inference, and retraining pipelines; build CI/CD workflows; manage MLflow tracking, registries, versioning, and governance; develop scalable model-serving APIs; orchestrate Databricks workflows; and containerize applications with Docker for Kubernetes deployment. Collaborate with data scientists, data engineers, and stakeholders to establish reliable MLOps architecture and operational practices.

Build and optimize batch and streaming data pipelines, semantic models, and transformations on Databricks using Python and Spark. Define data contracts, quality rules, and data SLOs for data products. Guide domain teams on data modeling and product boundaries, mentor engineers and analysts, and improve platform templates, tooling, and engineering practices.

The Data Engineer will design and implement ETL processes, optimize data queries, automate pipelines, and develop scalable data infrastructures.

The Data Engineer Lead will oversee team collaboration, lead projects, design ETL processes, mentor members, and optimize data infrastructure.