{"id":2124011,"url":"https://alion.io/job/altimetrik-data-engineer-with-ai","title":"Data Engineer with AI","company":{"id":1985,"name":"Altimetrik","domain":"altimetrik.com","url":"https://alion.io/company/altimetrik","size_band":"51-200","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":"contractor","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_city","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Kraków, Poland"],"countries":["PL"],"hiring_countries":["PL"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":64000,"max_usd":97000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":68},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Cortex","optional":false},{"name":"Databricks","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SharePoint","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"AutoGen","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"AWS Bedrock AgentCore","optional":true},{"name":"Azure","optional":true},{"name":"Confluence","optional":true},{"name":"CrewAI","optional":true},{"name":"Jira","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LLMOps","optional":true},{"name":"MLFlow","optional":true},{"name":"OpenAI","optional":true},{"name":"Power BI","optional":true},{"name":"Prometheus","optional":true},{"name":"Semantic Kernel","optional":true},{"name":"ServiceNow","optional":true},{"name":"Streamlit","optional":true},{"name":"Tableau","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-10-06T12:05:11Z","employer_posted_date":null,"last_verified_at":"2026-10-06T12:05:11Z","board_verified":false,"closed_at":null,"days_open":6,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":6},"description":"Altimetrik Poland is a digital enablement company. We deliver bite-size outcomes to enterprises and startups from all industries in an agile way to help them scale and accelerate their businesses. We are unique in Poland's IT market. Our differentiators are an innovation-first approach, a strong focus on core development, and an ability to attack the challenging and complex problems of the biggest companies in the world.\n\nWe are looking for Data Engineer with strong experience on embedded Agentic AI capabilities in Snowflake and Databricks. Our client is is a global healthcare company that provides solutions to meet the evolving needs of patients worldwide.\n\nYour experience:\n5+ years of experience in data engineering, AI engineering, analytics engineering, or cloud data platform delivery.\n\nStrong SQL and Python skills.\n\nExperience with agentic development practices, including the use of AI agents and coding assistants to accelerate solution design, prototyping, code generation, testing, documentation, deployment preparation, and iterative delivery of data and AI products.\n\nHands-on experience with Snowflake and/or Databricks, including data modeling, semantic layer design, data pipelines, performance tuning, and platform optimization.\n\nStrong knowledge of Snowflake Cortex, including Cortex Analyst, Cortex Search, Cortex Agents, LLM functions, and semantic models.\n\nStrong knowledge of Databricks Genie and the data modeling, metadata, governance, and Unity Catalog foundations required to enable natural-language analytics.\n\nDeep understanding of semantic layer modeling, business metrics, KPI definitions, hierarchies, dimensions, governed datasets, and AI-ready data products.\n\nExperience with data catalogues, metadata management, lineage, data ownership, business glossary, and data quality controls.\n\nExperience with structured and unstructured data, including enterprise tables, documents, PDFs, SharePoint/Teams content, logs, and business metadata.\n\nStrong understanding of Snowflake Warehouses and Databricks Clusters / SQL Warehouses, including sizing, workload isolation, autoscaling, performance tuning, and cost optimization.\n\nExperience with LLM model selection and cost optimization, including model evaluation, token usage, latency, context window, inference cost, and quality/cost trade-offs.\n\nAbility to assess and optimize LLM usage costs, including token consumption, caching strategies, prompt optimization, model routing, use-case-based model selection, inference cost monitoring, and balancing quality vs. cost.\n\nPractical experience with GenAI, AI agents, RAG, enterprise search, conversational analytics, or LLM-powered assistants.\n\nStrong knowledge of data governance, RBAC/RLS, masking, auditability, and secure enterprise data access.\n\nStrong communication skills, with the ability to explain technical decisions to data engineers, architects, product owners, governance teams, and business users.\n\nExperience in regulated enterprise environments where security, compliance, auditability, and data governance are critical.\n\nNice to have:\nExperience building embedded Streamlit applications, especially on Snowflake or Databricks, to expose AI agents, data products, validation tools, or business-facing workflows.\n\nExperience with Snowflake Streamlit, Databricks Apps, or similar lightweight application frameworks for data and AI products.\n\nExperience with cloud-native AI / Agentic AI services, including AWS SageMaker, AWS Bedrock, AWS AgentCore, Azure AI Foundry, Azure OpenAI, Google Vertex AI, or equivalent services for model development, model serving, agent orchestration, knowledge grounding, tool integration, monitoring, and enterprise-scale deployment.\n\nExperience with agent orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.\n\nExperience with MLOps / LLMOps, including MLflow, model registry, prompt versioning, evaluation pipelines, monitoring, and feedback loops.\n\nExperience with enterprise integrations such as Power BI, Tableau, Excel, Teams, SharePoint, ServiceNow, Jira, Confluence, CRM platforms, or workflow automation tools.\n\n🔥We grow fast.\n🤓We learn a lot.\n🤹We prefer to do things instead of just talking about them.\nIf you would like to work in an environment that values trust and empowerment... don't hesitate, just apply!\nTech stack\nPython\nSQL\nSnowflake\nDatabricks\nData 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