About Us
We're Deepwork Technologies, the team behind Floww.ai — an AI-powered platform that helps businesses and governments run their most complex work, from sales teams to government services. We're a fast-growing startup with a young, talented team of alumni from IITs and other premier institutes, building technology that runs complex real-world operations at scale.
Among the platforms we have built is BuildNow, the Telangana Government's Next-Gen Unified Building & Layout Approval System and one of India's most successful digital governance platforms.
About the Role
You’ll be responsible for the data stack end-to-end — the warehouse, transformation layer, metrics, and AI agents built on top of them. The transformation layer involves turning raw data into clean, structured, tested, and reliable data that can be used for reporting, analysis, and decision-making. You’ll work closely with leadership and product teams to understand what they need from the data and deliver it. You’ll be responsible for ensuring the data is accurate, timely, and dependable. This role is ideal for someone who thinks logically, works methodically, and is curious about what the data can tell you before anyone asks.
What You'll Do
- Be responsible for the data architecture — Design and manage how data is stored and organized in BigQuery, including schemas, partitioning, clustering, and DDLs.
- Build the data transformation layer — Convert raw data into clean, reliable, and tested data models using scheduled and version-controlled pipelines.
- Define and maintain data metrics — Maintain clear data definitions and quality checks so teams get consistent and reliable answers from the data.
- Answer business questions with data-backed insights — Work with Product and Operations teams to understand their requirements, analyze the data, validate the findings, and deliver meaningful insights with clear considerations. Provide data in required formats, such as Google Sheets and other business-ready formats, based on team requirements.
- Build useful dashboards and reports — Create dashboards and self-service data models that help teams find the information they need without depending on the data team for every request.
- Automate data work with AI agents — Build and improve AI-powered tools such as a Data Copilot that can answer questions in plain English, along with agents for dashboard building and routine data engineering tasks, with the right controls and safeguards.
- Be responsible for data costs — Optimize BigQuery queries and data structures to reduce unnecessary data processing and keep costs predictable.
- Follow data security and governance standards — Handle confidential and sensitive data responsibly and ensure all data processes follow security and governance requirements.
- Build and mentor the data team — As the data function grows, hire, onboard, and mentor data engineers while setting clear standards and best practices for the team.
What We're Looking For
- Systematic and organized — You work in a systematic and organized way, while keeping the broader business and system context in view.
- Logical and structured thinking — You use logic to break complex problems into clear and manageable parts, identify the key issues, and arrive at practical solutions.
- Adaptable & flexible — you adjust fast to changing requirements, new tools and shifting priorities.
- Clear communication — you ask the right questions and can disagree with a reason.
- Attention to Detail & Ownership — You maintain a strong focus on data accuracy and quality while taking ownership of tasks and driving them through to successful completion.
Skills & Qualifications
- 1-2 years in analytics engineering or data engineering — Production systems you can explain in detail: what you built, why you structured it that way, and what you would change.
- Advanced SQL on Google BigQuery, including DDL — Hands-on BigQuery specifically, not transferable SQL.
- Data modelling — Dimensional or equivalent modelling in dbt or a similar framework, with testing, version control, and a view on when to denormalise.
- BI and semantic modelling — Hands-on delivery in Looker Studio, Power BI, or equivalent, including the model behind the dashboard.
- Applied AI — Practical experience working with LLM APIs or AI agent frameworks, with a strong focus on grounding AI outputs and verifying the accuracy of what the model returns.
- Python — For pipeline automation, transformation, and exploratory data analysis (EDA).
- Data formats — Strong Google Sheets skills, plus working knowledge of CSV and JSON.
- Must be a Graduate from Top 100 NIRF colleges — as per the NIRF 2025 Engineering Rankings.