🚀 Hiring :- Lead Data Engineer 

Job Type :- Full-time
Work Mode :- Remote
Compensation :- ₹2–2.5 Cr per annum

About the Role

We are looking for an experienced Lead Data Engineer to architect, build, and scale a modern data platform from the ground up. This is a hands-on technical leadership role for an engineer who can transform fragmented operational and product data into reliable, accessible and actionable systems.

You will own the end-to-end architecture and implementation of data warehouses, ingestion pipelines, ETL/ELT workflows, data models and product analytics infrastructure. You will also build AI-ready data foundations that enable autonomous AI agents, self-service analytics, intelligent automation and data-driven decision-making across the organization.

Key Responsibilities

Build Data Infrastructure from Scratch :- Architect and implement scalable data warehouses, ingestion systems, ETL/ELT pipelines, data transformations and core data models.

Develop AI-Ready Data Platforms :- Create well-documented, structured, and trustworthy datasets and interfaces that AI agents can discover, query and utilize safely.

Data Integration & Transformation :- Consolidate fragmented operational and product data into consistent, high-quality sources of truth.

Data Modeling & Warehousing :- Design efficient data models, warehouse schemas, analytical datasets, and reusable data assets.

Enable Self-Service Analytics :- Build infrastructure that empowers engineering, product and business teams to access insights independently.

Product Analytics & Instrumentation :- Establish event tracking, analytics instrumentation, reporting systems and product performance measurement.

AI-Powered Automation :- Use AI coding agents and intelligent automation to accelerate pipeline development, testing, documentation, validation, monitoring and troubleshooting.

Data Quality & Governance :- Implement data quality checks, lineage tracking, observability, access controls, security, governance and reliability standards.

Scalability & Performance :- Optimize distributed data processing, query performance, pipeline efficiency and infrastructure scalability.

Technical Leadership :- Make architectural decisions, mentor engineers, establish engineering best practices and maintain high standards through hands-on execution.

Required Qualifications & Skills

Extensive hands-on experience building, deploying and operating production-grade data platforms.

Proven greenfield engineering experience, including personally designing and implementing a data platform from scratch.

Strong expertise in data engineering, data architecture, data warehousing, ETL/ELT, data integration and transformation pipelines.

Advanced proficiency in SQL and Python, supported by strong software engineering fundamentals.

Experience with modern data platforms and technologies such as Snowflake, Google BigQuery, Amazon Redshift, Databricks, dbt, Apache Airflow, Dagster or equivalent tools.

Strong understanding of data modeling, orchestration, distributed data processing, workflow management and pipeline optimization.

Experience implementing data quality frameworks, data lineage, observability, monitoring, governance, security and access management.

Knowledge of product analytics, event tracking, instrumentation, business intelligence and operational reporting.

Familiarity with AI-native engineering workflows and regular use of AI coding agents to automate development and repetitive data engineering tasks.

Ability to balance scalability, performance, maintainability, simplicity and engineering velocity.

Demonstrated technical leadership, architectural ownership, mentoring and engineering standards development.

Strong problem-solving, analytical thinking, communication and cross-functional collaboration skills.

Preferred Qualifications

Experience as a Founding Data Engineer or early engineering team member responsible for building an organization's first data platform.

Experience designing data infrastructure for AI-powered applications, autonomous agents and intelligent workflows.

Knowledge of semantic layers, metadata management, data catalogs, data discovery and AI-accessible enterprise data systems.

Experience implementing agent-ready data interfaces and structured access to enterprise datasets.

Proven ability to automate data pipeline development, testing, documentation, maintenance and validation using AI agents.

Experience with cloud data infrastructure, scalable data architectures, production debugging and performance optimization.

Ability to build pragmatic, future-ready systems without unnecessary complexity or overengineering.