{"id":3795478,"url":"https://alion.io/job/kadagency-global-data-engineer","title":"Data Engineer","company":{"id":4513095,"name":"KadAgency Global","domain":"kad.agency","url":"https://alion.io/company/kadagency-global","size_band":null,"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":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Lund, Sweden"],"countries":["SE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Grafana","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MQTT","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"TimescaleDB","optional":false}],"status":"live","first_seen_at":"2026-11-04T14:49:00Z","employer_posted_date":null,"last_verified_at":"2026-11-04T14:49:00Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"About the Role\nWe are looking for a Data Engineer to develop, maintain and improve the data infrastructure and backend services supporting our RE:Flex platform.\nThe role involves working with real-time IoT data, cloud infrastructure, databases and data pipelines to ensure reliable data collection, processing, storage and delivery.\nYou will work closely with backend developers, DevOps engineers, machine learning engineers and other teams across the business, operations and finance. Alongside hands-on development, you will contribute to technical improvements, code reviews, troubleshooting, documentation and knowledge sharing.\nKey Responsibilities\nData Engineering and Pipelines\nDevelop and maintain Python-based services for collecting and processing data from connected energy devices, including battery systems and meters.\nBuild and improve real-time and batch data pipelines using MQTT, Google Cloud Pub/Sub and other messaging technologies.\nIntegrate data from external APIs, energy market platforms and other relevant data sources.\nImplement data validation, error handling, retry mechanisms and safe backfilling to improve data quality and reliability.\nDevelop and maintain data processing workflows, including aggregation and transformation of time-series data.\nBackend Development and Cloud Infrastructure\nDevelop and maintain backend APIs and services for accessing real-time and historical platform data.\nWork with databases and storage solutions such as BigQuery, TimescaleDB, PostgreSQL and MongoDB, depending on application requirements.\nDevelop, deploy and maintain containerised applications using Docker and Kubernetes.\nContribute to cloud infrastructure improvements, primarily within Google Cloud Platform.\nSupport integrations between data services, backend applications and other platform components.\nData Quality, Monitoring and Reliability\nMonitor data pipelines, database performance and service reliability using tools such as Grafana and cloud monitoring services.\nInvestigate data inconsistencies, missing records, processing failures and other production issues.\nImprove data processing performance, scalability and operational stability.\nImplement appropriate data quality checks, logging, monitoring and alerting.\nSupport incident investigation, troubleshooting and implementation of technical fixes.\nEnergy Market and Business Data\nSupport data processing and integration for energy market operations, including market prices, schedules, measurements and settlement information.\nDevelop and maintain data workflows used for operational reporting, revenue calculations and customer invoicing.\nWork with market operations, finance and other business teams to understand data requirements and resolve discrepancies.\nHelp ensure data consistency and traceability across relevant systems and integrations.\nTechnical Collaboration and Documentation\nParticipate in technical planning, architecture discussions and code reviews.\nCoordinate with backend, DevOps, machine learning and other engineering teams on data-related implementations.\nSupport onboarding and knowledge sharing with other engineers.\nMaintain technical documentation, data flow descriptions and operational procedures.\nContribute to continuous improvements in development practices, deployment processes and platform reliability.\nRequired Qualifications\nRelevant education in Computer Science, Software Engineering, Data Engineering or a related technical field, or equivalent practical experience.\nAt least 5 years of professional experience in data engineering, backend development or a related software engineering role.\nStrong programming skills in Python and good knowledge of SQL.\nExperience developing data pipelines, backend services and REST APIs.\nExperience working with relational or NoSQL databases and cloud-based data processing.\nFamiliarity with messaging systems, streaming data or asynchronous processing.\nPractical experience with Docker and cloud infrastructure.\nUnderstanding of software development practices, version control, testing and debugging.\nGood communication skills and the ability to collaborate with technical and non-technical teams.\nProfessional working proficiency in English.\nMeriting Experience\nGoogle Cloud Platform, particularly Pub/Sub, BigQuery and Cloud Storage.\nMQTT, IoT telemetry or real-time data processing.\nTimescaleDB, PostgreSQL, MongoDB or other time-series and NoSQL databases.\nKubernetes, CI/CD pipelines and infrastructure automation.\nDagster, Airflow or similar data orchestration tools.\nMonitoring and observability tools such as Grafana and Prometheus.\nExperience with data reconciliation, reporting or invoicing integrations.\nKnowledge of battery energy storage systems, energy flexibility services or Nordic electricity markets.\nExperience supporting technical onboarding, documentation or cross-team coordination.\nWhat We Offer\nThe opportunity to contribute to Sweden's energy transition through real-world energy technology solutions.\nHands-on experience working with real-time data, cloud infrastructure and distributed energy systems.\nA collaborative engineering environment with opportunities to contribute to technical decisions and platform improvements.\nThe opportunity to work across data engineering, backend development and energy market 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