{"id":4320882,"url":"https://alion.io/job/myntra-senior-software-engineer-data-platform-engineering","title":"Senior Software Engineer Data Platform Engineering","company":{"id":1431,"name":"Myntra","domain":"myntra.com","url":"https://alion.io/company/myntra","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Hadoop","optional":false},{"name":"Incident Management","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Apache Kafka","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"dbt","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-11-14T17:22:40Z","employer_posted_date":null,"last_verified_at":"2026-11-14T17:22:40Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Senior Software Engineer – Data Engineering (3-5 Years Experience)\nMyntra Data Platform (MDP) About The Role\nMyntra Data Platform (MDP) powers the data foundation behind analytics, experimentation, personalization, machine learning, and critical business decisions across Myntra.\nWe are looking for a Senior Software Engineer – Data Engineering who is passionate about building scalable data platforms and production-grade data pipelines. You will work on large-scale batch and near real-time data processing systems, drive data quality and reliability, and contribute to the evolution of Myntra's modern lakehouse architecture.This role requires strong software engineering fundamentals combined with hands-on expertise in Apache Spark, SQL, Python, ETL development, workflow orchestration, data modeling, and modern data platforms.You will collaborate with platform, analytics, product, and engineering teams to build highly reliable and scalable data solutions that power business-critical use cases across the organization.\n\nWhat You'll Do\nData Pipeline Engineering\nDesign, develop, and maintain scalable ETL/ELT pipelines for large-scale data processing.\nBuild reliable data ingestion, transformation, validation, and publishing workflows.\nDevelop reusable components and frameworks to improve engineering productivity.\nImplement incremental processing, backfills, retries, and failure recovery mechanisms.\nEnsure data pipelines meet reliability, scalability, and performance requirements.\nApache Spark & Big Data Processing\nBuild and optimize large-scale data processing jobs using Apache Spark, PySpark, and Spark SQL.\nDevelop production-grade transformations, aggregations, and data enrichment pipelines.\nAnalyze Spark execution plans and optimize jobs for performance and cost.\nTroubleshoot issues related to partitioning, joins, data skew, shuffles, and memory utilization.\nFollow engineering best practices for efficient distributed processing.\nData Modeling & Warehousing\nDesign and implement scalable data models for analytics and reporting.\nBuild curated datasets supporting business intelligence and downstream applications.\nApply dimensional modeling concepts including facts, dimensions, star schemas, and SCDs.\nTranslate business requirements into reusable and maintainable data models.\nWorkflow Orchestration\nDevelop and manage workflows using Apache Airflow or equivalent orchestration platforms.\nDefine task dependencies, scheduling strategies, retries, and monitoring mechanisms.\nImprove operational visibility and reliability of data workflows.\nSupport self-service capabilities for pipeline onboarding and deployment.\nLakehouse & Data Platform Engineering\nBuild and operate workloads on Databricks and Spark-based platforms.\nWork with Delta Lake, Apache Iceberg, Parquet, or similar storage formats.\nOptimize storage layouts, partitioning strategies, compaction, and metadata management.\nContribute to platform capabilities that simplify data consumption and processing.\nData Quality & Governance\nImplement automated data quality validations and monitoring frameworks.\nDevelop checks for freshness, completeness, schema validation, and consistency.\nEnsure proper metadata management, lineage tracking, and discoverability.\nFollow data governance and security best practices.\nOperational Excellence\nMonitor pipeline health, SLAs, performance, and cost metrics.\nInvestigate production issues and perform root cause analysis.\nImprove platform stability through automation and continuous optimization.\nParticipate in on-call support and incident management when required.\nEngineering Excellence\nWrite clean, maintainable, and testable code.\nParticipate in code reviews and design discussions.\nContribute to engineering standards, best practices, and reusable frameworks.\nCollaborate with cross-functional teams to deliver high-quality solutions.\n\nWhat We're Looking For\nRequired Qualifications\n3-5 years of hands-on experience in Data Engineering or Software Engineering.\nStrong experience with Apache Spark, PySpark, and Spark SQL.\nProficiency in Python and strong coding fundamentals.\nExcellent knowledge of SQL and query optimization techniques.\nExperience building production-grade ETL/ELT pipelines.\nHands-on experience with Apache Airflow or similar workflow orchestration tools.\nGood understanding of data warehousing and data modeling concepts.\nExperience working with Databricks, Hadoop, or cloud-based data platforms.\nFamiliarity with Delta Lake, Apache Iceberg, or Parquet.\nStrong debugging, troubleshooting, and problem-solving skills.\nGood understanding of software engineering best practices, CI/CD, and version control systems.\nPreferred Qualifications\nExperience working with Kafka or other streaming technologies.\nExposure to near real-time data processing architectures.\nExperience with dbt or similar SQL transformation frameworks.\nKnowledge of modern lakehouse architectures and cloud-native data platforms.\nUnderstanding of data governance, metadata management, and lineage concepts.\nExposure to AI-assisted development tools and data engineering automation.\nExperience with Kubernetes, Docker, or cloud platforms such as GCP, AWS, or 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