Data Engineer

Who we are:

devx Labs is an AI-native consulting firm that fundamentally reimagines business operations around AI capabilities. We don’t just implement AI tools - we transform how enterprises operate through first-principles thinking and outcome-driven execution.

About the Role:

As a Data Engineer, you are the architect of the data foundation that powers every client decision transforming raw information into reliable, scalable, and AI-ready systems.

You design and own modern data platforms, build robust ETL pipelines, optimize data warehouses, and enable analytics that drive measurable business outcomes. Beyond writing code, you mentor engineers, establish engineering best practices, and gradually integrate AI into data workflows to unlock greater efficiency and intelligence. This is not just a "build pipelines" role you're the force behind scalable data infrastructure, trusted insights, and the next generation of AI-powered data engineering.

Data Infrastructure (70%)

• Design and build data warehouses usingBigQuery, Snowflakefor client analytics and reporting
• Create robust ETL/ELT pipelineshandling diverse data sources (APIs, databases, files, streaming)
• Build data models for analytics, BI dashboards, and business intelligence
• Optimize query performance, storage costs, and pipeline reliability
• Implement data quality frameworks, monitoring, and alerting
• Integrate with ERP systems (Frappe) and client business systems

AI & Modern Data (20%)

• Prepare data pipelines for AI/ML workloads(embeddings, training data, feature stores)
• Explore vector databases and semantic search for data discovery
• UseAI tools to accelerate pipeline development, data transformation, and documentation
• Build data APIs that AI agents and applications can consume

Team Leadership (10%)

• Mentor junior data engineers on best practices and architecture
• Review code and data models for quality and performance
• Participate in hiring and team growth initiatives
• Drive AI adoption within data engineering workflows

Technical Foundation:

• 4+ years of data engineering experience with production data warehouses
• Expert-level SQL and data modeling (star schema, dimensional modeling, data vault)
• Strong Python for data pipelines
• Hands-on with BigQuery, Snowflake, or Redshift
• ETL/ELT tools: Airflow, dbt, Fivetran, or custom pipeline frameworks
• Cloud platforms: AWS (Glue, Lambda, S3, RDS) or GCP (Dataflow, Cloud Functions)

Infrastructure & Tools:

• Docker, Kubernetes for containerized data pipelines
• CI/CD for data workflows
• Git version control for data code and transformations
• Experience with data quality and testing frameworks

AI-Native Mindset (Non-Negotiable):

• Already using AI tools (Claude Code, Cursor, Copilot) for 50%+ of development
• Can articulate how AI accelerates data pipeline development
• Excited about AI's role in data engineering, not threatened by it
• Bonus: Experience with vector databases, embeddings, or ML data prep

Problem-Solving:

• Can debug complex data quality issues across distributed systems
• Performance optimization mindset (queries, storage, compute)
• Ownership mentality: you see problems through to resolution

• AI tool credits and freedom to experiment with modern approaches.
• Performance-based appraisal at 6 months + annual appraisal cycle.
• Competitive pay with a structured CTC breakup.
• Work with clients across industries and domains.
• Hybrid model with work-from-home support and flexible working hours.
• No-cap leave policy — take time off responsibly, no fixed limits.
• Health insurance with spouse and kids coverage included.
• Yearly outings, and an open environment for innovation.