{"id":3104869,"url":"https://alion.io/job/albertsons-companies-senior-data-engineer-2","title":"Senior Data Engineer","company":{"id":1650,"name":"Albertsons Companies","domain":"albertsonscompanies.com","url":"https://alion.io/company/albertsons-companies","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":100,"open_postings":10,"ghost_share":0,"stale_share":0.1,"repost_share":0.1,"time_to_fill_p50_days":8,"computed_at":"2026-10-27T05:46:45Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, 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robust data engineering solutions.\nDevelop and optimise ETL/ELT processes using modern cloud, data warehousing, and data processing platforms.\nCollaborate with technical and business teams to translate complex requirements into robust data solutions.\nDiagnose and resolve complex production issues through root-cause analysis and system optimisation.\nDrive technology and process improvements by evaluating emerging tools and engineering best practices.\nEnsure adherence to enterprise architecture, security, compliance, and performance standards.\nMaintain high-quality technical documentation, user guides, and solution specifications.\n\nRequirements: \nExperience in data modelling, data engineering, and data integration.\nExperience leading engineering initiatives.\nProven experience with cloud tools such as GCP Dataflow, GCP Composer (Airflow), and GCP Cloud Storage.\nStrong expertise in cloud platforms: GCP.\nHands-on experience with data warehousing and processing technologies.\nExperience with data streaming tools: Kafka, Cloud Data Flow, Spark Streaming.\nStrong experience in data quality and observability platforms to automate quality coverage using iceDQ, Monte Carlo, or similar tools.\nStrong proficiency with high-level programming and query languages.\nExperience collaborating with distributed teams across time zones.\nSkills: BigQuery, Lakehouse (Iceberg), Spark, Python, SQL.\n\nAI Skills (Mandatory): \nProficiency in leveraging AI tools for daily engineering tasks to enhance productivity, optimise effort, and ensure cost-aware AI assistance.\nFamiliarity with Retrieval Augmented Generation (RAG) architectures, including semantic layers for data retrieval and grounding Large Language Model (LLM) responses.\nConceptual understanding of LLM-based applications (e. g., chatbots, Q& A systems), encompassing prompt engineering, context management, and response generation.\nExposure to agentic and multi-agent AI systems, including agent roles, tool utilisation, memory management, and workflow orchestration.\nPractical experience with LLM application frameworks (e. g., LangChain, Lang Graph, or Google GenAI tools) for prototyping and integrating AI-driven solutions.\n\nNice to Have: \nExperience with Power BI/ThoughtSpot, AI in data engineering, Stonebranch, CI/CD, Databricks and DevOps practices. \nFamiliarity with enterprise databases such as DB2 SQL Server, Oracle, and PostgreSQL.\nGoogle Cloud Data Engineer certification is preferred.\nExperience in retail domains (customer, stores, eCommerce, marketing, supply chain, and merchandising).\nSkills\nAirflow, BigQuery, Dataflow, ELT, ETL, GCP, Google Cloud Platform, Iceberg, Kafka, Python, SQL, Spark, data engineering, data integration, data 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