{"id":117255,"url":"https://alion.io/job/vega-blog-data-engineer-ai-team","title":"Data Engineer - AI Team","company":{"id":40788,"name":"Vega","domain":"vega.io","url":"https://alion.io/company/vega-blog","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Comeet","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":["Tel Aviv, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":61000,"max_usd":165000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":935},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Datadog","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"DuckDB","optional":false},{"name":"Kubernetes","optional":false},{"name":"Neo4j","optional":false},{"name":"pgvector","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Semantic Search","optional":false},{"name":"Spark","optional":false},{"name":"Trino","optional":false},{"name":"AWS","optional":true}],"status":"live","first_seen_at":"2026-08-24T14:06:53Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-22T16:32:01Z","board_verified":true,"closed_at":null,"days_open":59,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":58},"description":"Description\nVega is one of the fastest-growing startups in cybersecurity, redefining security analytics and operations with an AI-native platform for the SOC. We are building the next-generation operating system for security teams. Vega is already delivering real impact at some of the world’s largest organizations - improving detection, unlocking the value of their security data, and reducing cost and complexity. With HQs in New York and TLV, we're looking for people who want to be a part of the next rocket-ship in cyber.\nWe’re looking for a talented Data Engineer to join our AI team and build the data foundations that power our AI systems at scale. As a key member of the AI team, you’ll be responsible for designing, building, and operating the data layer that enables our AI models and agents to perform in production. This includes data ingestion, storage, transformation, monitoring, and defining the data strategy that ensures our AI applications receive high-quality, reliable, and timely data.\nWe’re seeking an ambitious Data Engineer who is passionate about building robust data infrastructure, working with large-scale and complex datasets, and partnering closely with AI engineers and researchers to translate model needs into production-grade data systems.\nWHAT YOU WILL DO\nDesign, build, and operate scalable data pipelines (batch + streaming) that power Vega’s AI systems in production.\nOwn the end-to-end data lifecycle: ingestion, storage, transformation, serving, and continuous improvement.\nBuild and maintain the data layer from raw data to semantically accessible data that enables AI models and agents to perform reliably at scale.\nEnsure high data quality and high-standard operation through monitoring, alerting, and validation checks.\nWork with modern storage systems (data lakes, warehouses, relational, graph, and vector databases) to support diverse AI workloads.\nPartner closely with AI engineers and researchers to translate model requirements into production-grade data infrastructure.\nDefine and evolve the data strategy to for AI applications.\nRequirements\nWHAT YOU WILL BRING\n4+ years of professional experience in data engineering or ML infrastructure roles.\nStrong experience designing and building data pipelines (batch and streaming) for large-scale production systems.\nHands-on experience with data storage systems such as data lakes, data warehouses, relational databases and graph databases.\nProven ability to build reliable, observable, and scalable data infrastructure, including monitoring, alerting, and data quality checks.\nDemonstrated ownership across the full data lifecycle from ingestion and modeling, to serving, monitoring, and continuous improvement.\nAbility to work independently, managing priorities effectively in a fast-paced, product-driven environment, according to a dynamic data strategy.\nExperience with modern data and infrastructure technologies such as DuckDB, dbt, Temporal, Trino, Spark, PostgreSQL, PGVector Neo4j, Datadog, Python, Go, Docker, and Kubernetes.\nExperience working with ML models and framework as part of data pipelines (e.g. text embedding models, vector databases, semantic search algorithms)\nNICE TO HAVE\nExperience building data infrastructure to support ML/AI systems, including feature extraction pipelines for downstream ML models and inference-time data access.\nBackground in working with high-volume or complex data sources such as logs, events, telemetry, or security data.\nFamiliarity with modern cloud platforms, preferably AWS, and cloud-native data tools.\nExperience collaborating closely with ML/AI engineers to translate model and research requirements into scalable data 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