Engineering Manager

Role Overview

The Engineering Manager will lead cross-functional engineering initiatives spanning Data Engineering, SaaS platform development, enterprise integrations, DevOps, Business Intelligence, and analytics ecosystems.

The ideal candidate should bring deep expertise in modern Data Engineering and AWS cloud technologies while also possessing strong exposure to MERN stack applications, API ecosystems, MCP, iPaaS, ETL/reverse ETL pipelines, connector development, and scalable distributed systems.

This is a highly technical leadership role requiring hands-on involvement in architecture, technical solutioning, engineering governance, scalability planning, and mentoring engineering teams.

We are specifically looking for someone who can:

• Guide teams technically and architecturally
• Drive scalable engineering practices
• Support complex solutioning and integrations
• Lead modern cloud-native data platform initiatives
• Bring strong ownership across engineering execution and delivery

Key Responsibilities

Engineering Leadership & Technical Direction

• Lead and mentor cross-functional engineering teams comprising Data Engineers, Software Engineers, DevOps Engineers, BI/Analytics professionals,and Full Stack Developers.
• Provide hands-on technical leadership across platform architecture, engineering design, integrations, and data systems.
• Drive engineering best practices, coding standards, scalability, security, and operational excellence.
• Conduct architecture reviews, code reviews, technical discussions, and solution design sessions.
• Act as the technical anchor for complex engineering and platform-related decisions.
• Foster a culture of ownership, accountability, collaboration, and continuous learning.

Data Engineering & Data Platforms

• Lead the architecture and development of scalable cloud-native Data Engineering platforms.
• Design and oversee large-scale ETL/ELT pipelines, reverse ETL, data ingestion frameworks, transformation systems, and data orchestration workflows.
• Build scalable and reliable structured/unstructured data processing systems.
• Drive implementation of data lakes, data warehouses, and analytics-ready data ecosystems.
• Ensure platform scalability, data quality, governance, observability, reliability, and performance optimization.
• Guide teams on best practices around data architecture, distributed processing, and real-time data engineering.

B2B SaaS Product Engineering

• Lead development of enterprise-grade B2B SaaS platforms with strong focus on scalability, resiliency, maintainability, and performance.
• Collaborate with Product and Business teams to translate requirements into scalable technical solutions.
• Drive modern engineering practices across backend services, frontend applications, APIs, integrations, and cloud infrastructure.
• Support multi-tenant platform architecture and enterprise-scale system design.

API Integrations, iPaaS & Connector Development

• Architect and guide development of enterprise integration frameworks and connector ecosystems.
• Lead API integration initiatives involving third-party enterprise platforms, marketing systems, analytics tools, CRM platforms, and cloud services.
• Design scalable REST APIs, integration services, orchestration layers, and reusable connector frameworks.
• Provide technical leadership around iPaaS architecture and integration best practices.
• Ensure secure, scalable, and high-performance interoperability across systems.

Cloud, DevOps & Platform Engineering

• Lead AWS-based cloud architecture and engineering initiatives.
• Guide teams on cloud-native architecture, scalability, resiliency, monitoring, and cost optimization.
• Drive DevOps maturity through CI/CD pipelines, infrastructure automation, observability, and deployment best practices.
• Support containerized and microservices-based platform development.
• Ensure engineering teams follow DevSecOps and platform reliability best practices.

Full Stack & Application Engineering

• Provide technical guidance on MERN stack application development and architecture.
• Support development of scalable frontend applications and backend microservices.
• Guide teams on modular architecture, API-first development, distributed systems, and event-driven architectures.
• Drive maintainability, performance optimization, and engineering quality standards.

Analytics & Business Intelligence

• Collaborate with Analytics and BI teams to build scalable reporting and decision-support ecosystems.
• Support implementation of analytics frameworks, dashboards, and business intelligence platforms.
• Enable engineering solutions that support data-driven insights and operational intelligence.

Required Qualifications & Experience

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related field.
• 12+ years of overall software engineering experience with strong exposure to enterprise B2B SaaS product development.
• 3–5+ years of experience in Engineering Management or Technical Leadership roles.
• Strong hands-on expertise in Data Engineering and cloud-native data platforms.
• Extensive experience with AWS cloud ecosystem and services.
• Strong experience designing and developing: ETL / ELT pipelines, API integrations, Connector frameworks, Data ingestion platforms, Distributed systems & Enterprise integration solutions
• Strong understanding of iPaaS platforms and integration architectures.
• Good understanding of MERN Stack technologies: MongoDB, Express.js, React.js & Node.js
• Experience with microservices architecture and event-driven systems.
• Strong exposure to DevOps practices, CI/CD pipelines, infrastructure automation, and containerized environments.
• Good understanding of Business Intelligence and Data Analytics ecosystems.
• Strong architectural thinking, technical problem-solving, and decision-making capabilities.
• Excellent communication, stakeholder management, and team leadership skills.

About MADTECH.AI

MADTECH.AI is the Decision Intelligence platform for mission-driven organizations and their partners.

Eliminate the burden of data wrangling, model building, data visualization, and optimization in one cost-effective AI-powered hub. By delivering sharper, on-demand insights, predictions, recommendations, and proactive problem-solving, the platform reduces data complexity empowering clients to make smarter, data-driven decisions faster and more affordably.

Headquartered in St. Petersburg, Florida, U.S.A. with global capability centers in Mysuru and Bengaluru, Karnataka, India, MADTECH.AI proudly offers platform support and Professional Services staffed with proven specialists who save clients money, time and achieve transparent, accountable performance while delivering extraordinary value.