Jobs at MUTT DATA

Design, implement, and maintain scalable backend microservices in Python. Own full service lifecycle, ensure high availability, performance, and security. Collaborate across teams on architecture, CI/CD, cloud deployment, and incident response in a regulated financial context.

Lead measurement and impact analysis for AI-powered products across the lifecycle. Define metrics, design A/B tests, build analytics frameworks, uncover product opportunities, present insights to leadership, and collaborate cross-functionally to drive data-informed product decisions.

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 responsive, accessible front-end interfaces for data and ML products. Collaborate with Product, Design, and Back End teams, own UX/UI decisions, integrate with APIs, improve front-end architecture and component libraries, and participate in code reviews and technical decisions.

The role involves developing batch and streaming data pipelines, mentoring teams, defining data quality rules, and enhancing data products using technologies like Databricks and Microsoft Fabric.

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

The Data Developer will implement data pipelines, research new tools, and collaborate with teams for smooth integration of data systems.

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

As a Data Engineer Lead, you will develop batch and streaming data pipelines, maintain data contracts, guide teams on data modeling, and mentor engineers.

As a Senior Data Engineer, you will develop batch and streaming data pipelines, maintain data contracts, and guide domain teams on data modeling, while mentoring other engineers.

As a Machine Learning Engineer, you will develop ML models, optimize performance, implement client requirements, and collaborate on technical solutions.

Lead the Machine Learning team, oversee project development, optimize ML systems and data pipelines, mentor team members, and align technical and business goals.

Owns end-to-end cloud data project delivery, including scope, planning, budgets, timelines, resources, stakeholder communication, team leadership, progress tracking, governance, risk mitigation, documentation, and quality improvement. Leads cross-functional data engineering and data science teams while managing cloud data engagements such as migrations, lake and warehouse builds, data engineering, and data science projects.

Design and evolve large-scale AWS data platforms for a banking data modernization project. Define architecture standards and patterns, build dbt models and Airflow pipelines on Redshift, implement data testing and observability, participate in design reviews, and collaborate with engineering and business stakeholders to ensure consistent adoption of best practices.

As a Data Engineer Lead, you'll lead data projects, design ETL processes, mentor team members, and collaborate with clients to deliver custom solutions.

Drive U.S. strategic growth by identifying, developing, and closing complex enterprise deals in the Databricks ecosystem. Build executive relationships, lead co-sell motions with partners, design account plans, and partner with Sales, Engineering, and Delivery to ensure successful execution and long-term value. Own CRM updates and relay market feedback to shape offerings and GTM strategy.

Design, build, and maintain batch and streaming data pipelines and semantic models on Databricks (Python/Spark). Define data contracts, quality rules, and SLOs; guide domain teams on data modeling; and mentor engineers and analysts while improving platform templates and tooling.

Design and deploy production-grade agentic LLM systems, build document understanding pipelines (OCR, image/table parsing), create evaluation sets for legal QA, control hallucinations/abstention, and translate legal/commercial stakeholder needs into system requirements.

Build and maintain Power BI dashboards, design and automate production-grade data transformations and pipelines using Databricks, PySpark, Delta Lake and Microsoft Fabric. Collaborate with data engineering and business stakeholders to define KPIs, monitor data quality, troubleshoot reporting issues, and document metrics and reporting processes.

Lead data architecture and functional design for large-scale data modernization in banking. Translate business needs into clear functional specs, facilitate design reviews, manage technical debt, ensure data testing/observability/CI-CD practices, and identify service growth opportunities for technical-commercial proposals.