{"id":3430718,"url":"https://alion.io/job/neogencode-technologies-pvt-ltd-senior-aws-data-engineer-data-lake-platform","title":"Senior AWS Data Engineer – Data Lake Platform","company":{"id":3798318,"name":"NeoGenCode Technologies Pvt Ltd","domain":"neogencode.com","url":"https://alion.io/company/neogencode-technologies-pvt-ltd","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","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":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":800000,"max":1800000,"currency":"INR","period":"year","gross":true,"usd_annual":18999},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Lambda","optional":false},{"name":"GraphQL","optional":false},{"name":"IAM","optional":false},{"name":"PostgreSQL","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Amazon Aurora","optional":true},{"name":"Apache HTTP Server","optional":true},{"name":"Apache Hudi","optional":true},{"name":"Apache Iceberg","optional":true},{"name":"Apache Kafka","optional":true},{"name":"dbt","optional":true},{"name":"Delta Lake","optional":true}],"status":"live","first_seen_at":"2026-10-28T07:42:42Z","employer_posted_date":null,"last_verified_at":"2026-10-28T07:42:42Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Job Title : Senior AWS Data Engineer – Data Lake Platform\nExperience : 5+ Years\nLocation: Gurugram Sector 43, Haryana\nWork Mode : Work From Office (5 Days a Week – Monday to Friday)\nOpen Positions : 8+\n\nAbout the Role :\nWe are looking for a skilled Senior AWS Data Engineer with strong hands-on experience in building, managing, and optimizing enterprise-scale data lake platforms on AWS.\nThe ideal candidate will have expertise in AWS data services, PySpark, Apache Airflow, and large-scale data pipeline development. The role involves designing end-to-end data ingestion and processing workflows, maintaining data quality, optimizing platform performance, and supporting analytics and downstream data consumption requirements.\n\nMandatory skills :\nAWS Data Lake, Amazon S3, EMR, Glue, Athena, Redshift, DMS, Lambda, RDS, IAM, PySpark, Apache Spark, Apache Airflow, Python, Advanced SQL, Data Modeling, Production Data Pipelines, Hasura/GraphQL, PostgreSQL, and Production Support.\n\nKey Responsibilities :\nDesign, develop, and maintain scalable AWS-based data lake platforms and end-to-end data pipelines.\nBuild data ingestion, staging, transformation, curated, and consumption layers using AWS services.\nDevelop and optimize data processing pipelines using PySpark, Apache Spark, and AWS EMR.\nImplement workflow orchestration and scheduling using Apache Airflow.\nWork with Amazon S3, AWS Glue, Glue Data Catalog, Amazon Athena, and Amazon Redshift for data storage, cataloging, processing, and analytics.\nDevelop efficient SQL queries and implement data modeling strategies for enterprise reporting and analytics.\nBuild and support data-serving solutions using Amazon RDS, AWS Lambda, API Gateway, and PostgreSQL-based platforms.\nMonitor pipeline performance, troubleshoot production issues, perform root-cause analysis, and ensure data pipeline SLAs.\nImplement data security, access controls, PII protection, and governance best practices across the platform.\nOptimize data processing performance, query execution, storage utilization, and AWS infrastructure costs.\nCollaborate with application, analytics, and business teams to deliver reliable and scalable data solutions.\n\nMandatory Skills :\nAWS Data Services : Amazon S3, EMR, Glue, Athena, Redshift, DMS, Lambda, RDS, and IAM.\nData Engineering : Strong hands-on experience building and maintaining enterprise data lake platforms.\nProgramming : Python, PySpark, and Apache Spark.\nOrchestration : Production experience with Apache Airflow.\nDatabase & SQL : Advanced SQL, data modeling, and query optimization.\nData Architecture : Experience with data ingestion, staging, transformation, curated layers, and consumption architecture.\nProduction Support : Pipeline monitoring, troubleshooting, root-cause analysis, and SLA management.\nAPI / Data Serving : Experience with Hasura or a similar GraphQL-based data-serving layer integrated with PostgreSQL.\nPlatform Ownership : Ability to independently manage data pipelines, improve platform reliability, and collaborate with cross-functional teams.\n\nGood-to-Have Skills :\nExperience with dbt for data transformation and modeling.\nKnowledge of data lakehouse architectures and table formats such as Apache Iceberg, Delta Lake, or Apache Hudi.\nExperience with Amazon Redshift workload optimization, distribution keys, sort keys, and Spectrum.\nKnowledge of Kafka and real-time or streaming data ingestion.\nExperience with PostgreSQL or Amazon Aurora as a data-serving layer.\nFamiliarity with data governance, compliance, and sensitive-data management.\nExperience in the BFSI or health insurance domain.\n\nIdeal Candidate Profile :\n5+ years of hands-on experience in AWS Data Engineering.\nStrong experience building and supporting production-grade AWS data lake platforms.\nPractical expertise in PySpark, Apache Airflow, SQL, and AWS data services.\nAbility to troubleshoot complex data pipeline issues and optimize performance.\nStrong understanding of data architecture, data modeling, and production support practices.\nCandidates with experience in enterprise data platforms and insurance or BFSI environments will be preferred.\nSkills\nAmazon Web Services (AWS), AWS Data Lake, Amazon S3, Amazon EMR, AWS Glue, AWS Athena, Amazon Redshift, DMS, AWS Lambda, AWS RDS, AWS IAM, PySpark, Apache Spark, Apache Airflow, Python, Advanced SQL, Data modeling, Production Data Pipelines, hasura, GraphQL, PostgreSQL, Production 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