Lead Data Engineer

Company Description

Aqilea is an IT and engineering consulting partner that helps companies get more out of their technology and operations. With teams in Stockholm and Bangalore, we work closely with our clients to build solutions that fit their needs - from software development, AI and infrastructure engineering to industrial automation and embedded systems.

We combine strong technical expertise with a practical, business-focused approach to help organizations modernize, improve security, and scale with confidence. Above all, we focus on long-term partnerships built on trust, quality, and real results.

With us, you have great opportunities to take real steps in your career and the opportunity to take great responsibility.

Role : Lead Data Engineer

Exp Range : 8 to 12 years

Work Location : Bangalore(Hybrid)

Notice : Immediate Joiners

Lead Data Engineer - Inventory Flow Assistant (Google Cloud / BigQuery / dbt)

About the role

We are looking for a highly experienced, hands-on Lead Data Engineer to join the Emerging Technology team and help build the next generation of AI-powered supply chain products.

You will be a key technical contributor in the development of the Inventory Flow Assistant (IFA), an enterprise-scale data and AI platform designed to improve inventory visibility, inventory health monitoring, decision intelligence and AI-driven recommendations across the supply chain.

This is a senior individual contributor role requiring deep technical expertise in Google Cloud, BigQuery, dbt, modern data architecture and large-scale data processing. You will be expected to design, build and own production-grade data products that serve machine learning models, AI agents and other business applications.

The ideal candidate combines strong architecture thinking with extensive hands-on development experience and can independently deliver complex solutions while coaching other engineers.

Key Responsibilities

Data Product Engineering

• Design and build enterprise-grade data products on Google Cloud Platform.
• Identify, assess and integrate data entities from existing BigQuery-based data products.
• Design scalable data models, semantic layers and business-ready schemas.
• Build and maintain dbt-based transformation frameworks.
• Deliver high-quality curated datasets optimized for analytical and operational use cases.

Data Pipeline Development

• Develop end-to-end ingestion, transformation and serving pipelines.
• Implement batch and near real-time processing patterns.
• Build delta-based ingestion mechanisms to detect source changes and process updates continuously.
• Ensure pipeline reliability, observability and operational excellence.

AI & Machine Learning Enablement

• Build data products serving Machine Learning models.
• Develop data foundations supporting AI agents and intelligent automation workflows.
• Ensure training, inference and feedback-loop data availability.
• Partner closely with Data Scientists and ML Engineers.

Scalability & Performance

• Design solutions capable of processing billions of records across distributed systems.
• Optimize BigQuery workloads for performance and cost efficiency.
• Implement partitioning, clustering and workload management strategies.
• Design for high throughput, resiliency and scalability.

Required Experience

Must Have

• 7+ years of data engineering experience.
• 3+ years working in Google Cloud environments.
• Deep expertise in BigQuery.
• Strong hands-on experience with dbt.
• Expert SQL skills.
• Strong Python development capabilities.
• Experience designing enterprise-scale data products.
• Experience building and maintaining end-to-end ETL/ELT pipelines.
• Experience working with machine learning and analytical platforms.
• Proven ability to handle very large datasets (billions of records).
• Experience designing highly scalable distributed systems.
• Strong Git, CI/CD and DevOps practices.

Technical Skills

Google Cloud

• BigQuery
• Cloud Storage
• Dataflow
• Pub/Sub
• Cloud Composer
• Cloud Run
• Vertex AI (preferred)
• IAM & Security

Data Engineering

• dbt
• SQL (expert level)
• Python
• Data Modeling
• Data Warehousing
• ETL / ELT
• Data Quality Frameworks

Large Scale Data

• Streaming architectures
• Event-driven systems
• Incremental processing
• Delta ingestion patterns
• Partitioning and clustering strategies
• Performance optimization

Preferred Domain Experience

• Supply Chain, Inventory or Logistics domain experience.
• Retail data platforms.
• AI/ML product development.
• Semantic modelling and data product thinking.

Experience supporting AI Agents or Retrieval-Augmented Generation (RAG) solutions.