About the Company

Cardone Ventures is one of the fastest-growing business services and software companies in the United States. In six years, the company has grown to over $225M in annual revenue, serves thousands of business owners, operates from offices in Phoenix and Miami, and employs more than 420 team members, including a technology organization of approximately 70 engineers, product managers, designers, data professionals, and technology leaders. We are building an AI-native operating platform that combines customer onboarding, workflow automation, operational data, analytics, benchmarking, and AI agents into a single system used by business owners and advisors to scale their companies.

Unlike most startups, we are not building in search of a business model. We are building against a large, growing customer base with real operational complexity, real data, and real business outcomes.

About the Role

We are building a founding engineering team in Hyderabad and are looking for a Staff /Sr. Staff Data Engineer to help design and develop our next-generation enterprise data platform.

This is not a traditional ETL or reporting role. You will build scalable data foundations that power analytics, AI-driven applications, customer onboarding, benchmarking, and operational workflows.

We are looking for engineers with strong analytical thinking, deep data modeling expertise, and hands-on coding skills. The ideal candidate understands not just how to process data, but what the data represents, how it should be modeled, and how to build reliable systems at scale.

As part of the founding team, you will influence technical architecture, solve complex engineering challenges, and help establish an AI-forward engineering culture.

Responsibilities

- - Design and build scalable data ingestion pipelines integrating APIs, SaaS platforms, databases, files, and event streams.
- - Develop and optimize distributed data pipelines using Python, PySpark, Apache Spark, and SQL.
- - Design dimensional models,



including fact and dimension tables, star schemas, slowly changing dimensions, and canonical data models.
- - Build standardized, reusable datasets supporting analytics, financial reporting, benchmarking, and AI applications.
- - Implement contemporary lakehouse architectures using Bronze, Silver, and Gold data layers.
- - Establish data quality, validation, reconciliation, lineage, and observability across data pipelines.
- - Optimize solutions for performance, scalability, reliability, and cost efficiency.
- - Write clean, reusable, production-quality code with automated testing and CI/CD practices.
- - Collaborate with product, AI, and platform engineering teams to translate business requirements into scalable solutions.
- - Leverage AI-assisted development tools to accelerate engineering, automate repetitive tasks, and improve productivity.
- - Take end-to-end ownership of engineering solutions, from design through production.

Qualifications

1. Analytical Thinking & Problem-Solving

- Strong clarity of thought with the ability to break down ambiguous problems into structured, logical solutions.
- Ability to ask the right questions, challenge assumptions, and clearly explain technical decisions and trade-offs.

2. Dimensional Modeling & Data Understanding

- Strong hands-on experience with fact and dimension tables, star schemas, data grain, relationships, and SCD Type 1 and Type 2.
- Ability to translate business requirements into scalable analytical models and understand the business meaning behind the data.

3. Python, PySpark & SQL





- Strong hands-on coding proficiency in Python, PySpark, and advanced SQL.
- Ability to independently write, debug, test, and optimize production-quality code.
- Experience with complex SQL queries, window functions, joins, aggregations, and reusable PySpark transformations.

4. Apache Spark & Data Architecture

- Strong understanding of distributed data processing, Spark execution, partitioning, shuffles, data skew, and performance optimization.
- Experience designing scalable data pipelines and lakehouse architectures.
- Understanding of data quality, schema evolution, incremental processing, and production reliability.

5. AI-Driven Engineering

- Experience using AI-assisted development tools such as GitHub Copilot, Claude, ChatGPT, or similar technologies.
- Ability to leverage AI for coding, debugging, testing, automation, and improving engineering efficiency.
- Curiosity and willingness to explore how AI can enhance data engineering workflows and intelligent applications.

6. Engineering Fundamentals & Ownership

- Strong software engineering fundamentals, including Git, CI/CD, testing, and modular code design.
- Ability to independently own technical solutions, collaborate effectively, and communicate clearly.
- 6–12 years of relevant experience in data engineering, software engineering, or distributed data systems.
- Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience

Why Join Us?

This is an opportunity to join a founding engineering team and help build a modern, AI-enabled data platform from the ground up. You'll work on challenging engineering problems, influence architectural decisions, adopt emerging technologies, and build systems that power real business outcomes.

We're looking for builders—not just engineers who can write pipelines, but engineers who can think critically, understand data deeply, and create scalable solutions.

📌 Staff Data Engineer (India)
🏢 Cardone Ventures
📍 India