{"id":3150841,"url":"https://alion.io/job/kleinanzeigen-com-data-engineer-analytics-products","title":"Data Engineer (Analytics & Products)","company":{"id":2949074,"name":"Kleinanzeigen.com","domain":"kleinanzeigen.com","url":"https://alion.io/company/kleinanzeigen-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":"HiBob","truth_index":null},"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":["Spain"],"countries":["ES"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":44000,"max_usd":95000,"period":"year","method":"role_country_seniority_unknown","sample_n":35},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"Looker","optional":false},{"name":"LookML","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"AI Agents","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Claude","optional":true},{"name":"Dimensional Modeling","optional":true},{"name":"GDPR","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"Rest API","optional":true},{"name":"SLI/SLO/SLA","optional":true}],"status":"live","first_seen_at":"2026-10-23T11:27:38Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-24T17:54:25Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Kleinanzeigen reaches 36M+ monthly users and is Germany’s leading online classifieds market. We are a sustainability-focused platform where you won't just write code; you will power the services that bridge our high-traffic backend with seamless user experiences.As a Data Engineer (Analytics & Products) at Kleinanzeigen, you start where the data need begins - not where the code does. You will work directly with product managers, analysts, and domain stakeholders to understand what decisions the data needs to support, how it will be consumed, and what already exists in the platform before writing a single line. From there, you design and build data assets that are homogeneous with the existing stack, cost-effective at scale, maintainable by the next engineer, and self-discoverable, so consumers can find, understand, and trust what you built without asking you.\nYou will own the full lifecycle: ingestion through Airflow DAGs, transformation across dbt stage, core, and report layers on Databricks, and the quality, documentation, and observability that make those assets production-grade. You stay current with Airflow and design DAG topology deliberately, knowing when to use dynamic task mapping, data-aware scheduling, the TaskFlow API, or a Cosmos dbt task group with Watcher mode for performance-critical pipelines. You understand Spark well enough to know why a dbt incremental model running in Databricks produces a full table scan instead of a partition filter push-down, how a poorly configured merge operation compounds into file fragmentation over time, and what to do about it, whether that means adjusting the incremental strategy, adding liquid clustering, running OPTIMIZE, or rethinking the model grain entirely. You write Python and shell scripts as naturally as SQL and follow engineering core principles: modularity, idempotency, testability, not because they are rules, but because they make your work last.\nYou operate in an AI-First engineering model: AI handles execution; you own intent, precision, and correctness. You use it to automate, to accelerate best practices, and to raise the quality bar across the team, not to ship faster with less judgment. You will collaborate closely with analysts, product managers, data platform engineers, and stakeholders across the company to ensure that the datasets we create and maintain in our data platform are reliable, trustworthy, and built to serve the decisions that matter.\nWhat we´re looking for\nRequiredAdvanced SQL: window functions, CTEs, query optimisation, partitioning and clustering, with enough depth to understand execution plans and diagnose performance issues\nHands-on production experience with dbt across the medallion architecture: models, tests, unit tests, macros, snapshots, YAML documentation and materialisation strategies\nExperience building and operating Airflow DAGs using Python operators, with the ability to design DAG topology deliberately for different pipeline requirements\nProficiency in Python and shell scripting for pipeline development, ingestion tasks and operational tooling\nSolid understanding of lakehouse concepts and data formats: Delta Lake for ACID-compliant storage, Parquet for columnar efficiency, and Avro for event-driven schema definition\nAbility to work independently: owning tickets from design through delivery, including incident response and backfills\nAbility to gather requirements from non-technical stakeholders, understand consumption patterns, and translate business needs into data models that are accurate, maintainable and cost-effective\nExperience defining and maintaining schema definitions (Avro/ODIN or equivalent schema registry)\nFluency with AI-assisted development workflows as part of daily practice: authoring intent specifications, verifying and owning AI-generated outputs, and contributing to shared context and tooling so the whole team benefits\nExperience with semantic layer concepts: defining reusable metrics, dimensions, and business logic that serve as a single source of truth across consumers (e.g. dbt Semantic Layer, Looker LookML, or equivalent)\nSpark comprehension at the level needed to understand how Databricks processes dbt models, read execution plans, and diagnose and fix performance issues such as full table scans, merge inefficiencies and file fragmentation\nEngineering fundamentals applied to data work: modularity, idempotency, testability and single responsibility as default practices, not afterthoughts\nExperience treating cost as a first-class constraint: identifying and acting on optimisation opportunities across compute, storage and pipeline design\nNice to HaveExperience with Kafka or event-driven ingestion\nFamiliarity with Salesforce, Google Ads, Meta, or other marketing/CRM APIs\nExperience with data modelling patterns and when to apply each: dimensional modelling, star schema, wide tables, one big table, slowly changing dimensions, and normalisation vs denormalisation tradeoffs\nExposure to compliance requirements in data engineering: data classification, retention policies and privacy-aware modelling\nExperience with cost optimisation in cloud data platforms\nExperience orchestrating multi-agent AI workflows for complex engineering tasks\nContribution to shared AI tooling, skills, or context files in previous roles\nWhat Makes You a Great FitProactive by default: you don't wait to be asked. You flag issues before they become incidents, own follow-ups after delivery, and look for ways to improve things beyond the scope of your ticket.\nGenerous with knowledge: you enjoy sharing what you know, whether that's through documentation, pair programming, code reviews, or supporting analysts and product teams in adopting the data platform.\nA team builder: you care about the people around you as much as the work. You contribute to a culture where the team gets better together, not just individually.\nAn architect of intent, not just a code translator: you define the what and why with precision, use AI to handle the how, and verify that the output actually solves the problem. You understand that AI commoditizes syntax production - your value is in T-shaped thinking that connects business context, data modeling, and technical constraints.\nQuality-first mindset enabled by AI: you treat comprehensive testing, documentation, and data quality checks as non-negotiable - precisely because AI has made them cheap to produce. Low test coverage is no longer a time constraint; it's a choice.\nWhat will you do\nData Modelling & dbt DevelopmentImplement dbt models across the medallion architecture applying the right materialisation strategy for each layer and use case - incremental, full refresh or snapshots - with consistent naming conventions, YAML documentation, metadata tagging, unit tests to validate critical business logic, and reusable macros for common transformation and data replication patterns\nBuild and refactor models for different business areas of the company, including for example marketing performance, product metrics, C2C transactions, monetisation, vibrancy, and trust & safety\nAuthor reusable macros and apply consistent naming conventions, YAML documentation, and metadata tagging (billed, retention, gdpr)\nDesign data models that are homogeneous with the existing stack, built at the right grain for the use case, cost-effective to run, and self-discoverable without needing the author to explain them\nPerform cost-aware modelling: clustering strategies, warehouse sizing, incremental scan reduction, and pre-aggregation layers\nData Ingestion & Pipeline EngineeringBuild and maintain Airflow DAGs using Python operators, designing DAG topology deliberately by choosing execution patterns, dependency structures, and sensor logic that match the operational requirements of each pipeline\nDesign sensor logic for pipeline dependencies, including intraday vs daily completeness checks and DST-aware temporal handling\nOperate Cosmos dbt task groups within Airflow, including DAG splitting, warehouse selection, and Cosmos version upgrades\nIntegrate new data sources end-to-end by building reliable, fault-tolerant connections across heterogeneous endpoint types including REST APIs, event streams, database connectors and file-based sources, with error handling, retry logic, and security patterns that make each integration production-safe from day one, unit testing every operator and transformation component where possible, and defining SLAs and SLOs that set clear expectations on data freshness, completeness, and availability for downstream consumers\nWork with data in the right format for each layer: Avro for event-driven ingestion schemas, Parquet for efficient columnar storage, and Delta for ACID-compliant lakehouse tables with time travel and schema evolution\nData Quality & ReliabilityWrite dbt tests (not_null, unique, accepted_values) and semantic row-count checks to catch data quality issues before they reach consumers\nAuthor unit tests for critical business logic using dbt's unit test framework\nOwn data quality across the full lifecycle: design and maintain quality check pipelines combining dbt tests, semantic validation and custom logic, monitor pipeline health proactively before issues reach downstream consumers, diagnose and resolve root causes across schema drift, silent failures, duplicate data and idempotency issues, and plan and execute backfills safely with pre-validation, before and after checks and documented rollback criteria\nApply governance standards consistently across every pipeline and data asset you own, ensuring compliance requirements, data classification, retention policies and ownership accountability are traceable and maintained as the platform evolves\nContribute to the reliability of the platform beyond your own work, flagging systemic issues, improving shared patterns, and leaving every codebase and dataset in better shape than you found it\nCost OptimisationIdentify and implement cost reduction opportunities: warehouse downsizing, incremental model rewrites, DAG consolidation, unused table deprecation\nEnsure governance rules for compliance and cost ownership are applied consistently across pipelines and data assets - including tagging standards (business_unit, business_area, team) in Airflow and dbt\nEvaluate and act on compute cost signals from Databricks query cost tables\nStakeholder Collaboration & Knowledge SharingPartner with analysts, product managers and domain teams to translate business requirements into accurate, reusable data models, and enable them to build and own their own data assets through patterns, templates, PR reviews and pair programming\nProvide consultancy and training to product teams on data ingestion and transformation patterns\nSupport analysts in adopting dbt for report-layer development, including onboarding, PR review, and pair programming\nEnable teams to build their own data products: provide patterns, templates, and consultancy so analysts and product teams can develop and own report-layer assets independently\n Define and document reusable metrics, dimensions and business logic in the semantic layer, and capture institutional knowledge including domain assumptions, architectural decisions and data lineage context so it lives in the codebase and not in people's heads\nParticipate in cross-team alignment on data model standards, shared definitions and platform evolution, representing the data engineering perspective in decisions that affect multiple domains\nAI-First Engineering\nReview and verify all AI-generated code before submitting for peer review - you own correctness, not the AI\nMaintain machine-readable context files (CLAUDE.md) in repositories you own, capturing architectural constraints, naming conventions, and domain-specific rules for AI agents\nUse AI tooling as a force multiplier: author intent specifications, verify AI-generated outputs with full ownership of correctness, contribute to shared skills and context files, and use automation...","description_format":"text","description_chars":13921,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Flexible schedule","Gym membership","Health insurance","Parental leave"],"hiring_locations":[{"name":"Spain","iso":"ES","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Internet Services","Marketplaces","Online Classifieds"],"lifecycle":[{"event":"open","at":"2026-10-23T12:16:35Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":48,"reasons":["conf:0","velocity","win:early"],"computed_at":"2026-10-24T06:02:35Z"},"pay":null,"html_url":"https://alion.io/job/kleinanzeigen-com-data-engineer-analytics-products","json_url":"https://alion.io/job/kleinanzeigen-com-data-engineer-analytics-products.json","meta":{"generated_at":"2026-10-24T19:30:01Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":28579,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-25T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2949074},"rest":"https://alion.io/mcp/rest/get_company?id=2949074"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fkleinanzeigen-com-data-engineer-analytics-products"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fkleinanzeigen-com-data-engineer-analytics-products"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fkleinanzeigen-com-data-engineer-analytics-products"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/kleinanzeigen-com-data-engineer-analytics-products\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fkleinanzeigen-com-data-engineer-analytics-products"}]}