Senior Solution Engineer

Straive; Mumbai; Hybrid; INR 4,000,000 - 8,000,000; Full time

Job summary

Straive in Mumbai is seeking a hands-on senior solution engineer to design, build, and operate robust cloud-native data and AI solutions. You will own end-to-end delivery from architecture through deployment, with a focus on production-grade systems and governance across AWS and Snowflake.

You will collaborate with business and technical stakeholders to translate requirements into scalable data products, apply modern engineering practices, and leverage AI-assisted development tools to accelerate

Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, AI/ML, or related technical discipline.
• 7+ years of hands-on software engineering, solution engineering, or data engineering experience delivering production-grade systems in enterprise
• Experience building and operating data products, cloud services, or AI-enabled solutions with measurable business outcomes
• Deep hands-on AWS experience and core services including compute, storage, networking, IAM, security, orchestration, monitoring, serverless/event-driven architectures; AWS certifications preferred
• Deep Snowflake experience: data modelling, SQL tuning, pipeline integration, access control, cost/performance optimisation, data sharing, governance
• Strong Python/Java, with solid design principles, APIs, automated testing, packaging, dependencies, production maintainability
• Familiarity with AWS AI services (Bedrock, etc.) and agent-based AI patterns; experience with AI-assisted development tools
• Experience with governance, risk, and security considerations in data platforms
• Strong collaboration and communication with business and cross-functional teams
• Preferred: background in financial industry, with understanding of data sensitivity and regulatory expectations

Responsibilities

• Design, build, and operate production-grade software and data solutions end-to-end from problem definition to deployment and monitoring.
• Design and implement reliable, scalable data pipelines and data products using AWS and Snowflake across diverse data sources.
• Model, curate, and optimise Snowflake datasets, schemas, and data structures for enterprise platforms.
• Apply strong software engineering practices including clean code, automated testing, CI/CD, observability, and secure development.
• Partner with stakeholders to translate requirements into robust data solutions and enable advanced analytics and AI use cases.
• Use AI-assisted engineering as a standard part of daily development to accelerate coding, testing, and documentation while maintaining quality.
• Build cloud-native integrations on AWS leveraging compute, storage, networking, security, orchestration, and AI services where appropriate.
• Own deployment, release, and production operations including troubleshooting, performance tuning, incident resolution, and code reviews.

Skills

Python

Java

AWS

Snowflake

Data engineering

CI/CD

AI/ML tooling

Education

Bachelor's or Master's in Computer Science/related

Tools

AWS

Snowflake

GitHub Copilot

CI/CD tooling

Job description

We are looking for a hands‑on senior solution engineer who can design, build, and operate robust cloud‑native

data and AI solutions. The ideal candidate combines strong software engineering fundamentals with deep

practical experience in AWS and Snowflake.

You will have the following responsibilities:

• Design, build, and operate production‑grade software and data solutions end‑to‑end, from problem definition and architecture through implementation, deployment, monitoring, and continuous improvement.
• Design and implement reliable, scalable, secure, and well‑governed data pipelines and data products using AWS and Snowflake across structured, semi‑structured, and unstructured data sources.
• Model, curate, and optimise Snowflake datasets, schemas, and data structures in line with enterprise platform standards, ensuring performance, quality, consistency, and usability for downstream consumers.
• Apply strong software engineering practices, including clean code, modular design, automated testing, CI/CD, observability, secure development, and maintainable architecture.
• Partner with business and technical stakeholders to translate requirements into robust data solutions, prioritise delivery, and identify opportunities to enable advanced analytics and AI use cases.
• Use AI‑assisted engineering as a standard part of daily development work to accelerate coding, refactoring, documentation, testing, debugging, and solution exploration while maintaining strong engineering judgement and quality standards.
• Build cloud‑native integrations and automation on AWS, making effective use of services such as compute, storage, networking, security, orchestration, event‑driven architectures, and managed AI services where appropriate.
• Own deployment, release, and production operations, including troubleshooting, root‑cause analysis, performance tuning, incident resolution, peer code reviews, pair programming, and reuse of proven engineering patterns.

You will have the following qualifications:

• Bachelor or Master degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence / Machine Learning, or a related technical discipline.
• 7+ years of professional experience in a hands‑on software engineering, solution engineering, or data engineering role, with a proven track record of delivering production‑grade systems in enterprise environments.
• Demonstrated ability to build and operate data products, cloud services, or AI‑enabled solutions with measurable business outcomes and clear operational ownership.
• Deep hands‑on AWS experience is required, including practical knowledge of core services for compute, storage, networking, identity and access management, security, orchestration, monitoring, and serverless or event‑driven architectures. AWS certification is preferred, ideally AWS Certified Solutions Architect Associate, AWS Certified Data Engineer Associate, or AWS Certified Machine Learning Engineer - Associate.
• Deep hands‑on Snowflake experience is required, including data modelling, SQL performance tuning, pipeline integration, access control, cost/performance optimisation, data sharing, and platform governance. SnowPro® Core Certification or advanced Snowflake certifications are a plus.
• Strong proficiency in Python and/or Java, with solid understanding of software design principles, APIs, automated testing, packaging, dependency management, and production maintainability.
• Experience with AWS AI services, including Amazon Bedrock, and familiarity with agent‑based AI solution patterns, retrieval‑augmented generation, model evaluation, guardrails, and responsible AI practices is preferred.
• Demonstrated habit of using AI‑assisted engineering tools such as GitHub Copilot, Claude, Cursor, or similar tools as part of everyday development to improve productivity, code quality, testing, documentation, and delivery speed.
• Familiarity with harness engineering or similar AI‑assisted development concepts, including structuring prompts, evaluation loops, reusable development workflows, automated checks, and feedback mechanisms to improve reliability, repeatability, and engineering quality.
• Strong hands‑on engineering mindset, with a focus on code quality, sound design decisions, maintainability, and effective collaboration in team‑based environments.
• Strong familiarity with the software development lifecycle, Git‑based workflows, CI/CD, infrastructure‑as‑code concepts, automated testing, DevOps practices, and production support.
• Ability to translate ambiguous business problems into clear technical scopes, iterative delivery plans, and measurable success criteria.
• Comfortable working with sensitive and confidential data, and partnering with governance, risk, and security stakeholders to embed controls from the start.
• Strong collaboration and communication skills, with the ability to work closely with business stakeholders and cross‑functional technology teams.
• Preferred: background in the financial industry, with an understanding of financial markets, data sensitivity, regulatory expectations, and enterprise risk controls.