{"id":892447,"url":"https://alion.io/job/amazon-jobs-principal-data-engineer","title":"Principal Data Engineer","company":{"id":19,"name":"Amazon","domain":"amazon.com","url":"https://alion.io/company/amazon","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":78,"open_postings":4548,"ghost_share":0,"stale_share":0.885,"repost_share":0.003,"time_to_fill_p50_days":29,"computed_at":"2026-11-08T05:47:12Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"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":{"min_usd":36000,"max_usd":76000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":20},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Apache HTTP Server","optional":false},{"name":"Apache Hudi","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Cassandra","optional":false},{"name":"CI/CD","optional":false},{"name":"Data Vault","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Grafana","optional":false},{"name":"Hadoop","optional":false},{"name":"Java","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Redis","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"Splunk","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"dbt","optional":true},{"name":"Feature Store","optional":true},{"name":"Great Expectations","optional":true},{"name":"MLFlow","optional":true}],"status":"live","first_seen_at":"2026-09-14T07:49:32Z","employer_posted_date":null,"last_verified_at":"2026-09-14T07:49:32Z","board_verified":false,"closed_at":null,"days_open":55,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":55},"description":"We are looking for an experienced Principal Data Engineer to lead the design, development, and optimization of large-scale, cloud-native data platforms that power business-critical analytics, AI/ML, personalization, supply chain, merchandising, and customer experiences across Walmart's global ecosystem. As a principal engineer, you will drive technical strategy, establish engineering best practices, mentor senior engineers, and collaborate with cross-functional teams to build highly scalable, secure, and reliable data solutions capable of processing petabytes of structured and unstructured data.\nResponsibilities:\nArchitect enterprise-scale data platforms and distributed data processing systems.\nDesign and build scalable batch and real-time data pipelines.\nLead data architecture decisions across multiple engineering teams.\nDevelop high-performance ETL/ELT frameworks using modern big data technologies.\nBuild cloud-native data solutions leveraging Azure, GCP, or AWS.\nDesign robust streaming architectures using Kafka and Spark Streaming.\nOptimize data storage, partitioning, indexing, and query performance.\nBuild reusable data engineering frameworks and platform services.\nEnsure data quality, governance, security, lineage, and compliance.\nPartner with product, data science, ML engineers, and business stakeholders.\nDefine engineering standards, coding practices, and architecture guidelines.\nMentor senior engineers and provide technical leadership across organizations.\nDrive platform modernization initiatives and cloud migration programs.\nConduct architecture reviews and production readiness assessments.\nImprove system observability, monitoring, scalability, and resiliency.\nLead incident resolution and performance optimization efforts.\nWhat You'll Build: Enterprise Data Lake Platforms, Real-time Streaming Pipelines, Customer Data Platforms, Supply Chain Analytics Platforms, Pricing and Merchandising Data Systems, AI/ML Feature Engineering Pipelines, Retail Analytics Platforms, Recommendation and Personalization Data Infrastructure, and Data Governance Platforms.\nRequirements:\nBachelor's or master's degree in computer science, engineering, or a related field.\n10+ years of experience in data engineering or big data platform development.\nStrong expertise in Python and Java or Scala.\nExtensive experience with Apache Spark.\nStrong knowledge of distributed computing architectures.\nExperience designing large-scale data lakes and data warehouses.\nHands-on expertise in SQL and NoSQL databases.\nExperience with Apache Kafka and event-driven architectures.\nExpertise in Airflow or enterprise workflow orchestration tools.\nStrong understanding of data modeling (dimensional, star, snowflake, and data vault).\nExperience building cloud-native data platforms.\nExcellent understanding of data governance and metadata management.\nStrong software engineering fundamentals, including OOP, design patterns, and system design.\nExperience working with CI/CD pipelines and Infrastructure as Code.\nExcellent stakeholder management and leadership skills.\nTechnical Skills:\nProgramming Languages: Python, Java, Scala, SQL.\nBig Data Technologies: Apache Spark, Hadoop, Hive, HDFS, Apache Kafka, Spark Streaming, Delta Lake, Apache Iceberg, Apache Hudi.\nCloud Platforms: Microsoft Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS).\nData Warehousing: Snowflake, BigQuery, Azure Synapse, Amazon Redshift.\nWorkflow and Orchestration: Apache Airflow, Azure Data Factory, Databricks Workflows.\nDatabases: PostgreSQL, MySQL, Cassandra, MongoDB, Redis.\nDevOps and Infrastructure: Docker, Kubernetes, Terraform, GitHub Actions, Jenkins.\nMonitoring: Prometheus, Grafana, Splunk.\nPreferred Qualifications:\nExperience building enterprise data platforms serving billions of records daily.\nStrong expertise in lakehouse architecture.\nExperience with the Databricks ecosystem.\nExposure to AI/ML feature engineering pipelines.\nExperience implementing Data Mesh or Data Fabric architectures.\nKnowledge of privacy, governance, and regulatory compliance.\nExperience working in large-scale retail or e-commerce environments.\nStrong understanding of distributed systems and high-availability architectures.\nLeadership Expectations:\nProvide technical vision across multiple engineering organizations.\nInfluence architecture decisions and engineering roadmaps.\nMentor Staff and Senior Engineers.\nDrive engineering excellence and operational maturity.\nLead design reviews and technical governance.\nChampion innovation and adoption of modern data technologies.\nCollaborate effectively with Product, Platform, Security, and Infrastructure teams.\nNice to have: Databricks, Unity Catalog, dbt, MLflow, Feature Store, Kubernetes Operators, Data Contracts, OpenLineage, Great Expectations, Iceberg 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