{"id":3444208,"url":"https://alion.io/job/dat-data-engineer-ii","title":"Data Engineer II","company":{"id":9174,"name":"Dat","domain":"dat.com","url":"https://alion.io/company/dat","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Welcome to the Jungle","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","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":14000,"max_usd":38000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":21},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Copilot","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false}],"status":"live","first_seen_at":"2026-10-29T06:36:00Z","employer_posted_date":null,"last_verified_at":"2026-10-29T06:36:00Z","board_verified":false,"closed_at":null,"days_open":8,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":8},"description":"The core responsibilities for the job include the following:\n\nData Pipeline Development and Engineering: \nELT Process Implementation: Assist senior engineers in designing, building, testing, and maintaining cloud-native ELT (Extract, Load, Transform) data pipelines, ensuring data is reliably loaded into the Snowflake and S3 Iceberg layers.\nPipeline Architecture: Contribute to the design of scalable, modular data pipelines that support incremental loads, backfills, and reprocessing with minimal manual intervention.\nTransformation with dbt: Develop and maintain data models using dbt (data build tool) for data cleaning, aggregation, and transformation and utilize the Snowflake data warehouse.\nPython Scripting: Utilize Python to build custom data extraction scripts, implement monitoring tools, and contribute to general automation efforts.\nPipeline Reliability: Build in retry logic, alerting, and failure-handling patterns so pipelines degrade gracefully and self-recover where possible.\n\nWorkflow Orchestration and Automation: \nAirflow DAGs: Learn to author, schedule, and monitor data workflows defined as Directed Acyclic Graphs (DAGs) in Apache Airflow.\nPipeline Scheduling: Integrate and orchestrate dbt runs and other pipeline tasks within Airflow to manage dependencies and execution timing.\nAutomation-First Mindset: Actively look for repetitive, manual, or error-prone steps across the data lifecycle and automate them—from data ingestion to deployment to reporting.\nAutomated Testing and Deployment: Contribute to automated test suites and deployment scripts that reduce manual QA and release effort.\n\nCI/CD and Version Control: \nGit Workflow: Use Git for version control, following branching, code review, and pull request best practices.\nGitHub Actions: Build and maintain GitHub Actions workflows to automate testing, linting, dbt builds, and deployment of data pipeline code.\nContinuous Integration: Ensure new pipeline and model changes are automatically tested and validated before merging, catching issues before they reach production.\nContinuous Deployment: Support automated promotion of dbt models and pipeline code across environments (dev, staging, and production).\n\nAI-Enabled Engineering: \nAI-Assisted Development: Use AI coding assistants to accelerate development, generate boilerplate, and speed up code review and refactoring.\nAI-Augmented Documentation: Leverage AI tools to help draft and maintain technical documentation for data models, DAGs, and pipeline logic.\nApplied Curiosity: Stay current on emerging AI-assisted data engineering tools and workflows, and bring suggestions for improving team efficiency.\n\nData Quality, Testing, and Monitoring: \nData Quality: Implement data validation and testing frameworks using features of dbt (e. g., uniqueness, non-null checks) to ensure high data quality and accuracy within Snowflake data marts.\nTroubleshooting: Monitor data pipeline health, troubleshoot failed Airflow tasks and dbt runs, and quickly resolve data flow issues.\nDocumentation: Maintain clear and current technical documentation for data models, Airflow DAGs, and pipeline logic.\n\nRequirements: \nExperience: 3-5 years of professional experience in a data engineering, analytics engineering, or similar technical role (including relevant internship experience).\nEducation: Bachelor's Degree in Computer Science, Information Technology, Engineering, or a Related Quantitative Field.\n\nTechnical Proficiency: \nRequired: Strong proficiency in Python.\nHands-on experience with a cloud data warehouse, preferably Snowflake.\nFamiliarity with data transformation concepts and tools, DBT a bonus.\nBasic experience creating or running jobs/workflows using an orchestration tool like Apache Airflow.\nExperience with Git and CI/CD pipelines, ideally GitHub Actions.\nComfort using AI-assisted coding tools in a professional engineering workflow.\nSoft Skills: Strong problem-solving abilities, excellent attention to detail, and a proactive, collaborative approach to teamwork.\n\nKey Technologies: \nData Warehouse: Snowflake/Data Lake.\nTransformation: dbt (data build tool).\nOrchestration: Apache Airflow.\nCI/CD: Git, GitHub Actions.\nProgramming: Python.\nAI-Assisted Development: LLM-based coding assistants (e. g., 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