Data Platform Engineer

About QiCap QiCap.ai is a quantitative investment firm building systematic equity investment products for Indian and global markets. Our trading strategies combines financial data, quantitative signals, AI-driven analytics, and disciplined portfolio construction to deliver differentiated public markets investing solutions. We are a high-performance, close-knit team that combines investment expertise, data science, software engineering, and market experience to build scalable, technology led investment products.

The Role: Data Platform Engineer We are looking for a hands-on Data Platform Engineer to own the data, tooling, and internal systems layer. This role goes beyond traditional data engineering. You will be responsible not only for data ingestion, cleaning, validation, and workflow automation, but also for building internal web-based tools, dashboards, APIs, analytics utilities, and research workflow systems. This is an ideal role for someone who enjoys building reliable data systems and practical tools that serious users depend on every day. This is not a quant research role. You will work closely with the investment team and understand the research workflow, but success in this role will be measured by platform reliability, data quality, automation, usability, and speed of execution.

Key Responsibilities 1. Data Engineering & Infrastructure

• Own ingestion, cleaning, validation, storage, and monitoring of financial and market datasets.
• Work with structured and unstructured data from sources such as Bloomberg, Refinitiv, NSE/BSE, broker feeds, fundamentals, corporate actions, indices, portfolio data, and alternative data sources.
• Maintain scalable databases and data stores such as PostgreSQL, MongoDB, object storage, or similar systems.
• Build reliable data models and curated datasets for research, production, analytics, and reporting.
• Implement strong data quality checks, exception handling, reconciliation, and audit trails. 2. ETL, Workflow Automation & Production Runs
• Build, monitor, and maintain automated workflows using Airflow or similar orchestration tools.
• Ensure daily data and model workflows run accurately and on time.
• Create monitoring, logging, alerts, and failure-recovery processes for critical jobs.
• Convert recurring notebooks, scripts, and manual processes into robust production workflows.
• Support release management, version control, testing, and deployment of new data/model workflows. 3. Internal Tools, Dashboards & Web Applications
• Build internal web-based tools and dashboards for research, portfolio monitoring, risk views, sales analytics,nand operations.

QiCap.ai | Data Platform Engineer – Stratos Develop lightweight applications using tools such as Streamlit, Dash, Flask, FastAPI, Django, or similar frameworks. Create internal APIs and reusable data access layers for researchers and business users. Build one-off analytics tools and prototypes quickly, while ensuring useful tools can be hardened into reusable systems. 4. Software Engineering & Codebase Ownership Maintain and improve the Python-based data and tooling codebase. Write clean, modular, testable, and maintainable code. Implement Git workflows, code reviews, documentation, logging, testing, and CI/CD practices. Improve reliability, reproducibility, packaging, and deployment hygiene across research and production systems. Work with cloud infrastructure, containers, and basic DevOps workflows where required. 5. Collaboration & Ownership Work closely with Quant Research, Portfolio Management, Product, Sales, and Operations teams. Translate ambiguous internal requirements into practical tools and workflows. Interface with data vendors, APIs, brokers, and external technology partners. Document systems, processes, assumptions, and recurring workflows. Take end-to-end ownership of critical data and platform components.

Ideal Candidate Profile Must-Have Qualifications - 5–8 years of hands-on experience in data engineering, data platform engineering, backend engineering,analytics engineering, or internal tools engineering. 8+ years experience candidates welcome, if open to taking on enriching Individual Contributor role in a strong quant team.

• Strong programming skills in Python.
• Strong SQL skills and good understanding of data modeling and database design.
• Experience building and maintaining data pipelines, APIs, schedulers, and automated workflows.
• Experience with Pandas, NumPy, Polars, or similar Python data libraries.
• Experience with Git, Linux, debugging, logging, and production issue resolution.
• Ability to build simple internal web applications, dashboards, APIs, or tools.
• Strong ownership mindset and comfort working in a small, high-accountability team.
• Ability to work directly with senior internal users and convert business/research needs into working tools.

Preferred Qualifications

• Experience in financial services, fintech, asset management, hedge funds, prop trading, brokerage, or market-data platforms.
• Exposure to financial datasets such as equities, indices, corporate actions, fundamentals, portfolio holdings, risk data, broker data, or macroeconomic data.
• Hands-on experience with Airflow, Dagster, Prefect, or similar orchestration tools.
• Experience with Streamlit, Dash, Flask, FastAPI, Django, or similar frameworks.
• Experience with PostgreSQL, MongoDB, Redis, object storage, or time-series databases.
• Familiarity with AWS, GCP, Azure, Docker, CI/CD, or basic DevOps practices.
• Exposure to Bloomberg, Refinitiv, FactSet, NSE/BSE, broker APIs, or similar market-data systems.
• Understanding of investment workflows, portfolio analytics, trading lifecycle, or research systems.
• Education in Computer Science, Engineering, Mathematics, Statistics, or a related field from a reputed institution.

Personal Traits QiCap.ai | Data Platform Engineer – Stratos

• High ownership and reliability.
• Strong attention to detail.
• Enjoys building systems that others depend on.
• Comfortable with both new builds and ongoing maintenance.
• Practical, hands-on, and delivery-oriented.
• Low-ego team player who communicates clearly.
• Interested in financial markets, but not looking to use this role simply as a transition into quant research.
• Comfortable working in a fast-paced, intellectually demanding environment.

What Success Looks Like

• After 6–12 months, the successful candidate will have:
• Improved reliability and monitoring of data pipelines.
• Reduced manual intervention in daily research and production workflows.
• Built internal tools that researchers, PMs, and business teams use regularly.
• Created cleaner, more accessible datasets and APIs.
• Improved documentation, reproducibility, and deployment hygiene.
• Become the trusted owner of the data and internal tools platform.

Why Join Us?

• Work at the intersection of AI, data engineering, software tools, and capital markets.
• Build the data and systems backbone of a live systematic investment platform.
• Directly contribute to investment products used by real clients.
• Work closely with senior investment professionals, quant researchers, and product leaders.
• Operate in a high-accountability, low-hierarchy environment with significant ownership.
• Competitive compensation and long-term growth opportunity.