Key responsibilities:

- Architect and build scalable, reliable data pipelines and platformssoftware solutions for data products, ensuring performance, quality, and long-term sustainability.
- Own data solutions end-to-end from translating business objectives into technical designs through implementation, deployment, and production support.
- Design and implement data workflows that are reproducible, testable, and scientifically rigorous, embedding validation frameworks, monitoring, lineage, and observability into every stage.
- Enable advanced analytics and AI/ML use cases by building software solutions infrastructure that supports experimentation, versioning, and production-grade pipeline and model deployment.
- Lead architectural decisions and influence technical prioritisation, partnering closely with product and delivery teams to align engineering effort with business impact.
- Act as a technical authority within the team, elevating engineering standards and driving best practices in data management, governance, and cloud-native development.
- Engage senior stakeholders, clearly communicating complex technical trade-offs and recommendations in business-relevant terms.
- Collaborate cross-functionally with engineers, data scientists, analysts, and product leaders to deliver high-impact solutions for institutional investors and private markets clients.

What we are looking for:

- Proven experience building and operating scalable software systems for data processing workflows and platforms, with deep expertise in Python and SQL across databases such as Snowflake and Postgres.
- Hands-on experience with up-to-date software engineering practices, including version control (Git), CI/CD pipelines, automated testing frameworks, and containerisation and orchestration (Docker, Kubernetes).
- Experience working in cloud environments (AWS or Azure),



including infrastructure provisioning and automation using Infrastructure as Code (e.g., Terraform).
- Demonstrated ability to design production-grade systems that balance performance, scalability, reliability, security, and maintainability.
- Experience enabling or supporting advanced analytics and AI/ML use cases in production environments.
- A rigorous, data-driven mindset – comfortable using analysis, benchmarking, and experimentation to guide technical decisions and architectural trade-offs.
- Strong understanding of data validation, testing strategies, and code quality practices, with confidence applying diverse code and data testing techniques across data and application layers.
- Ability to operate autonomously and drive technical solution design end-to-end, taking ownership of outcomes.
- Experience collaborating effectively across engineering, data science, product, and design teams to deliver high-impact solutions.
- Excellent written and verbal communication skills, with the ability to influence stakeholders at all levels and translate complex technical concepts into clear, business-relevant language.
- A proactive, curious, and resilient mindset — motivated to explore new technologies, tackle ambiguous problems, and continuously improve systems and ways of working.

Desirable skills include:

- Experience with AI-related technologies and products; familiarity with using AI coding assistants.
- Experience working with financial market data, investment analytics, or private markets datasets.
- Experience designing and building data platformssoftware and/or data platforms in regulated or financial services environments.
- Experience supporting ML lifecycle management (model versioning, experiment tracking, model deployment pipelines); familiarity with tools such as MLflow, feature stores, or model serving frameworks.
- Experience productionising statistical or quantitative models.

📌 Application Engineer (Pune)
🏢 hackajob
📍 Pune