Mareana
Website:
mareana.com
Job details:
Principal Technical Architect
Bangalore / Full-Time
Job Description
We're looking for a hands-on Principal Technical Architect to own the architecture of a data-intensive, multi-tenant enterprise SaaS platform. The platform ingests high volumes of data from multiple enterprise systems, transforms and governs it through data pipelines, and exposes it through configurable application capabilities, workflows, APIs, and analytics — increasingly powered by AI and agentic systems.
The ideal candidate will have strong hands-on expertise in enterprise application architecture, distributed systems, multi-tenant SaaS, large-scale data platforms, data pipelines, configuration-driven applications, and analytical applications.
This role is not a people-management position. We are looking for a hands-on technology leader who can personally drive architecture, solve complex technical problems, make critical design decisions, and influence engineering teams through technical depth and leadership.
Key Responsibilities (What You'll Do)
Platform & Application Architecture
- Own end-to-end technical architecture: application services, APIs, workflows, integration, and analytical capabilities.
- Define clear boundaries between application, data, integration, and platform layers; establish reusable architectural patterns across product modules.
- Design for high availability, scalability, security, performance, and maintainability; modernize the architecture as the product and customer base scale.
Data-Intensive & Pipeline Architecture
- Architect ingestion, transformation, and consumption of large data volumes from multiple enterprise source systems — batch, near-real-time, event-driven, and CDC.
- Define data lake / lakehouse / warehouse and operational data store strategies; own data quality, lineage, metadata, governance, partitioning, and retention.
- Build scalable, observable data pipelines (Spark, Databricks, Kafka, cloud-native data services) that scale independently of application workloads and are optimized for performance and cloud cost.
Multi-Tenant SaaS Architecture
- Design tenant isolation strategies across application, data, compute, storage, and security layers.
- Define tenant-aware configuration, quotas, resource management, onboarding, provisioning, and lifecycle management.
- Architect for horizontal scalability with predictable per-tenant performance and cost.
Configuration-Driven Architecture**
- Design metadata- and configuration-driven frameworks for business rules, workflows, data mappings, UI behavior, and analytics.
- Define configuration versioning, validation, and dependency management so the platform evolves without heavy custom development.
- Keep configuration-heavy architecture maintainable and performant as customers and configurations grow.
Analytical, AI & Agentic Architecture
- Architect analytical capabilities — dashboards, KPIs, self-service reporting — using pre-computation, caching, semantic layers, or direct query as appropriate, without impacting transactional performance.
- Design and embed AI/ML and GenAI capabilities into the platform: LLM integration, RAG pipelines, prompt and context engineering, vector stores, and model serving.
- Architect agentic systems — multi-step autonomous/semi-autonomous agents, tool-use and function-calling frameworks, agent orchestration, memory, and guardrails — for workflow automation and intelligent decisioning.
- Evaluate and integrate emerging AI/agentic frameworks (e.g., LangChain, LangGraph, Semantic Kernel, MCP) where they add measurable product value.
Distributed Systems, Cloud & Governance
- Design cloud-native, microservices and event-driven systems with fault tolerance, retries, circuit breakers, idempotency, and graceful degradation.
- Lead architecture and design reviews for major initiatives; establish architecture principles, standards, and reference patterns.
- Partner closely with Product Management, Engineering, Data Engineering, DevOps, Security, and QA; mentor senior engineers through technical influence, not direct management.
- Drive technology evaluations and proof-of-concepts, including for AI and agentic tooling.
Requirements
- 15+ years designing and building enterprise software, with a Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Hands-on experience in Python / Java, and React.Js is a must.
- Proven track record as a genuinely hands-on Software/Solution Architect or Principal Engineer — someone who still prototypes, reviews code, and gets into implementation detail, not just diagrams and documents.
- Experience architecting and scaling multi-tenant, data-intensive enterprise SaaS platforms for global customers.
- Deep expertise in distributed systems and cloud-native architecture (AWS, Azure, or GCP).
- Extensive experience with data pipelines, data engineering architectures, and strong SQL/database architecture skills.
- Experience designing platforms that combine transactional, operational, and analytical workloads.
- Practical, hands-on experience building or architecting AI/ML, GenAI, or agentic capabilities into production enterprise applications.
- Experience with configuration- or metadata-driven enterprise applications.
- Strong understanding of APIs, microservices, and event-driven integration.
Technical Expertise
The candidate should have deep expertise in several of the following areas:
Application Architecture: Microservices, Distributed systems, Domain-Driven Design, Event-driven architecture, REST/API architecture, Workflow/orchestration, Configuration and metadata-driven architecture, Caching and distributed caching, Application performance optimization.
Prog Lang: Python / Java & React.JS
Data Architecture: Data lake / lakehouse architecture, Data warehouse architecture, ETL / ELT, Batch and streaming pipelines, Distributed data processing, Data modeling, Data governance and lineage, Large-scale analytical workloads
Data Technologies: Databricks, Apache Spark, Kafka, Delta Lake, Snowflake, Azure Data Factory / AWS Glue / equivalent, Cloud-native data services, SQL and NoSQL databases
Cloud & Platform: AWS / Azure , Kubernetes, Docker, Serverless architectures, Infrastructure as Code, CI/CD, Observability and monitoring, Cloud security
Nice to Have
- Domain experience in manufacturing, supply chain, healthcare, pharma, or life sciences.
- Experience integrating data from ERP, MES, LIMS, CRM, or similar enterprise systems.
- Data governance and enterprise data management experience.
- Exposure to SOC 2, ISO 27001, GxP, or similar compliance environments.
- Experience working with graph databases (Neo4j or equivalent) and building data semantic layers.
Ideal Candidate Profile
We're looking for a genuinely hands-on Principal/Staff-level architect, not an architecture manager. You've spent your career building and scaling complex, data-intensive enterprise products, understand multi-tenant SaaS at scale, and are equally comfortable whiteboarding a system end-to-end and going deep into implementation — including modern AI and agentic techniques — when the problem calls for it.
A strong candidate can design an architecture spanning multiple enterprise data sources, large-scale data pipelines, a multi-tenant application platform, configuration-driven capabilities, AI/agentic features, APIs, workflows, and analytics — and defend the engineering trade-offs behind every major decision.
About Mareana
Founded in 2015, Mareana is an AI-powered software company with the mission of accelerating digital transformation in manufacturing, supply chain, and sustainability via our connected intelligence platform.
Mareana’s platform uses AI/ML to rapidly connect disparate, siloed data across the entire business process, allowing our customers to shift their time and effort from data preparation to making complex business decisions intuitively, in real time.
Our customers are market leaders in life sciences, chemicals, and general manufacturing who have realized over a billion dollars in business value by leveraging our platform. Our ethos of continuous innovation has been recognized by Gartner, who named us a “Cool Vendor in AI”. We have also been featured as a thought leader in Silicon Review and Manufacturing Insights magazine.
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