{"id":3666441,"url":"https://alion.io/job/ascendion-dataetl-engineer-32","title":"Data/ETL Engineer","company":{"id":46972,"name":"Ascendion","domain":"ascendion.com","url":"https://alion.io/company/ascendion","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"B","score":79,"open_postings":25,"ghost_share":0,"stale_share":0.84,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-11-05T05:54:30Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"hybrid","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":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Lambda","optional":false},{"name":"DynamoDB","optional":false},{"name":"GitHub","optional":false},{"name":"IAM","optional":false},{"name":"PostgreSQL","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Terragrunt","optional":false},{"name":"Amazon CloudWatch","optional":true},{"name":"CI/CD","optional":true},{"name":"ETL/ELT","optional":true},{"name":"Kanban","optional":true},{"name":"Scrum","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-11-02T11:27:38Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-05T03:02:47Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"About the Role\"\n\"Data Engineer\"\nHybrid -Bangalore\nYears of EXP - 6+years\nThe below mentioned is the illustrated job description, that we should refer to for a Data Engineer Profile, (highlighted are the key skills we need)-\nThe ideal candidate will have expertise in Glue, Data Catalog, Athena, DynamoDB, Lambda, S3, RDS (PostgreSQL), ECS, SQS, SNS, CloudFront, and IAM roles. Strong proficiency in Python, PySpark, Terraform, and Terragrunt, coupled with experience in Agile environments and GitHub, is essential. You will play a pivotal role in building robust data pipelines and infrastructure to support analytics and business intelligence initiatives.\nRequired Skills and Qualifications\n AWS Expertise:\nProficiency in AWS Glue, Data Catalog, Athena, DynamoDB, Lambda, S3, RDS (PostgreSQL), ECS, SQS, SNS, and CloudFront.\nStrong understanding of IAM roles and security best practices.\nProgramming and Frameworks:\nAdvanced proficiency in Python and PySpark for data processing.\nSolid understanding of SQL for querying relational and non-relational databases.\n Infrastructure as Code(good to have)\nHands-on experience with Terraform and Terragrunt for infrastructure automation.\n Development Practices:\nExperience in Agile methodologies (Scrum, Kanban) and version control systems like GitHub.\nFamiliarity with CI/CD pipelines and DevOps practices.\nOther Skills:\nStrong analytical and problem-solving skills.\nExcellent communication and collaboration abilities.\nAbility to work independently and in a team-oriented environment.\nKey Responsibilities\nData Engineering and Processing:\nDevelop, deploy, and manage scalable ETL/ELT pipelines using AWS Glue and PySpark.\nWork with AWS Data Catalog for metadata management and data discovery.\nOptimize query performance and data analysis workflows using AWS Athena.\nImplement serverless data workflows using AWS Lambda for real-time and batch data processing.\n Database and Storage Management:\nDesign and manage relational databases on AWS RDS (PostgreSQL) and non-relational databases on DynamoDB.\nStore, manage, and optimize data in AWS S3 for performance, security, and cost efficiency.\n Infrastructure as Code (IaC) (good to have):\nBuild and deploy AWS infrastructure using Terraform and Terragrunt.\nAutomate the provisioning of scalable, secure, and reliable resources.\n Cloud Services and Integrations (good to have):\nManage containerized data applications on AWS ECS for efficient deployment and scaling.\nDesign and implement event-driven architectures using AWS SQS and SNS.\nConfigure AWS CloudFront for secure, low-latency content delivery.\n Security and Access Management (should understand how its implemented):\nDesign and implement IAM roles and policies to ensure secure access and compliance.\nEnsure data security, including encryption at rest and in transit.\nCollaboration and Agile Practices:\nCollaborate with cross-functional teams, including data scientists, analysts, and developers, to understand data requirements.\nActively participate in Agile ceremonies, such as sprint planning, standups, and retrospectives.\nVersion Control and Continuous Integration:\nUtilize GitHub for version control, collaboration, and CI/CD workflows.\nEnsure best practices in code management and deployment processes.\n Monitoring and Optimization:\nMonitor and troubleshoot data workflows and AWS resources using AWS CloudWatch and other tools.\nOptimize data pipelines and resource configurations for performance and 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