Myntra is India's leading fashion and lifestyle platform, where technology meets creativity. As pioneers in fashion e-commerce, we've always believed in disrupting the ordinary.

We thrive on a shared passion for fashion, a drive to innovate to lead, and an environment that empowers each one of us to pave our own way. We're bold in our thinking, agile in our execution, and collaborative in spirit.

Here, we create MAGIC by inspiring vibrant and joyous self-expression and expanding fashion possibilities for India, while staying true to what we believe in.

We believe in taking bold bets and changing the fashion landscape of India. From our humble beginnings as a customization company in 2007 to being technology and fashion pioneers today, Myntra is going places and we want you to take part in this journey with us.

Working at Myntra is challenging but fun - we are a young and dynamic team, firm believers in meritocracy, believe in equal prospect, encourage intellectual curiosity and empower our teams with the right tools, space, and opportunities.

Technical Lead – Data Engineering

Myntra Data Platform (MDP) | Bengaluru | 6+ Years

Myntra Data Platform (MDP) powers the data foundation behind analytics, experimentation, personalization, ML and critical business decisions across Myntra.

We are looking for a hands-on Technical Lead – Data Engineering to build and evolve large-scale data processing, ETL and orchestration platforms, while helping shape the next generation of AI-native and agentic data engineering at Myntra.

This is a senior individual-contributor role requiring deep technical ownership—from architecture and production-quality code to performance optimization, troubleshooting and operational excellence.

The ideal candidate combines strong software engineering fundamentals with deep expertise in Spark, SQL, ETL, workflow orchestration, data modeling and modern lakehouse architectures.

Design, build and optimize large-scale batch processing systems using Apache Spark, PySpark and Spark SQL.

Build production-grade pipelines handling high-volume transformations, joins and aggregations.

Diagnose data skew, shuffles, memory pressure, inefficient joins and partitioning issues.

ETL & Data Pipeline Engineering

Build reliable ETL/ELT pipelines across ingestion, transformation, validation, enrichment and publishing.

Own pipelines from architecture development deployment production operations.

Orchestrate complex dependencies across Spark, SQL and data-processing workloads.

Build self-service capabilities for developing,



deploying and operating data pipelines at scale.

Optimize partitioning, file sizing, compaction, metadata, Spark and SQL workloads for performance and cost.

Build platform capabilities that simplify compute, storage and data consumption.

SQL, dbt, Data Transformation & Data Modeling

Develop and optimize complex production-grade SQL and Spark SQL transformations.

Build modular transformation models using dbt or equivalent frameworks.

Establish standards for SQL quality, testing, documentation and dependency management.

Design scalable relational, dimensional and lakehouse data models.

Translate business and analytical requirements into reusable data models across raw, curated and consumption-ready layers.

Agentic Data Engineering

Build AI-native data engineering capabilities across the end-to-end pipeline lifecycle.

Design AI-assisted workflows that translate requirements into data models, SQL/Spark transformations and executable pipelines.

Apply LLMs/agents to SQL and pipeline generation, code review, testing, documentation and optimization.

Build intelligent Spark/SQL optimization and pipeline RCA using execution plans, job metrics, logs, dependencies and historical performance.

Automate generation of data-quality checks, tests, documentation and lineage metadata.

Build natural-language/semantic interfaces for data discovery and safe pipeline creation/modification.

Design human-in-the-loop controls, validation, guardrails and evaluations for AI-generated SQL, code and pipelines.

Build reusable agent frameworks, tools and APIs, not isolated AI demos.

Data Quality & Governance

Embed data quality into pipelines with automated freshness, completeness, schema and consistency checks.

Design controls for PII, access management, auditing and sensitive data.

Ensure AI-generated and human-authored pipelines follow the same quality, security and governance standards.

Monitor failures, SLAs, freshness, data quality, performance and cost.

Lead troubleshooting and root-cause analysis of complex incidents.

Lead architecture/design reviews and review Spark, Python and SQL implementations.

Mentor engineers across Spark, ETL, SQL,



data modeling and production engineering.

Drive responsible adoption of AI-assisted engineering.

6+ years of hands-on Data Engineering / Software Engineering experience building production data systems at scale.

- Deep expertise in Apache Spark / PySpark, Spark SQL and Spark internals.
- Strong programming skills in Python, Java or Scala.
- Advanced SQL, query optimization and performance-debugging skills.
- Strong experience building production-grade ETL/ELT pipelines.
- Experience with Airflow, Dagster or equivalent orchestration platforms.
- Experience with Databricks or comparable Spark-based platforms.
- Experience with dbt or SQLMesh
- Strong understanding of data modeling and lakehouse architecture.
- Experience with Parquet, Delta Lake and/or Apache Iceberg.
- Experience with data quality, metadata, lineage and governance.
- Strong production ownership—from design and implementation through operations and RCA.
- Experience building LLM-powered developer tools, AI agents or engineering automation.

Understanding of LLMs, tool/function calling, RAG, structured generation and agentic workflows.

Experience applying AI to SQL generation, data discovery, pipeline generation, debugging or optimization.

Experience with metadata catalogs, semantic layers, lineage or knowledge graphs for AI context.

Experience building evaluations and guardrails for AI-generated SQL/code.

Experience building multi-tenant/self-service data platforms, frameworks or SDKs.

Experience with Kubernetes, cloud infrastructure and Unity Catalog or similar governance platforms.

You will build the engineering foundations that enable data engineering at Myntra scale — Spark processing, orchestration, ETL frameworks, data modeling, lakehouse architecture, governance and production reliability.

At the same time, you will help redefine data engineering for an agentic world — where agents understand data and dependencies, accelerate the journey from intent to production pipelines, automate repetitive engineering work, and operate within strong human-controlled guardrails.

We are looking for someone comfortable moving between Spark execution plans, Python, SQL, Airflow/Dagster DAGs, dbt models, metadata and platform architecture — and excited about making these workflows dramatically better with AI.

Build the data platform of today — and help define how data engineering will work tomorrow.

data platform, design, apache, spark, kafka, problem solving skills

📌 Tech Lead - Data (Bengaluru)
🏢 Myntra
📍 Bengaluru