Role Description Become part of the Flexibility (VPP) team, which builds a state-of-the-art virtual power plant solution based on pooling flexible residential DERs (e.g. batteries and EVs). By utilizing accurate forecasts and intelligent control, this solution unlocks the direct market participation of household DERs in a robust way. This service, which employs both deterministic steering and data-driven forecasting algorithms, is continually being enhanced and scaled to keep pace with the ever-increasing number of customers using this service. The Flexibility team is looking forward to growing together with you and bringing this feature to many more customers. Qualifications - Production-Grade Track Record: Hands-on experience working on real-world projects with a proven history of delivering robust data solutions into production. - Focus on Stabilization: Full ownership of system reliability, actively monitoring and stabilizing workflows to ensure continuous, high-quality operations. - Time-Series Expertise: Comfortable working with large-scale time-series data and understanding the unique structural and processing challenges it presents. Requirements - Programming & Data Wrangling: Solid experience writing clean, maintainable code for complex data manipulation and transformation tasks. Proficiency in Python, Polars, and Pandas is preferred, with strong proficiency in equivalent languages or data wrangling frameworks highly welcome. - Database Mastery: Strong SQL skills, with heavy reliance on ClickHouse—experience here is a highly valued asset. - Cloud & Infrastructure: Practical experience provisioning and managing scalable infrastructure using AWS, Kubernetes, and Terraform. - Workflow Management: Experience in building and orchestrating complex data pipelines and ML workflows. Familiarity with Metaflow, Argo, and MLflow, or comparable orchestration and model lifecycle tools is fully transferable. - Rigorous Validation: Thorough testing, validation, and benchmarking of models and data products prior to deploying them into live environments. - Observability: Maintain clear visibility over production systems and possess practical experience setting up metrics and alerting dashboards using Grafana. Benefits - Stimulating and developing work environment. - Opportunity to grow with the team and bring features to more customers. Company Description IT-bemanning är ett Stockholmsbaserat IT-konsultföretag, specialiserat på att leverera kvalitativ kompetens inom området IT-support, IT-drift samt IT-rekrytering. Vi är ett personligt IT-konsultföretag som värdesätter den enskilda konsultens egenskaper. IT-bemanning är ett dotterföretag till IT-konsultföretaget Xperta AB och som är specialiserade inom området IT-infrastruktur.

Role Description You'll set the technical direction for how b.well ingests, transforms, and delivers healthcare data at scale on our FHIR-based platform—owning our hardest problems (large-scale pipeline performance, data quality and de-duplication, terminology normalization, and reliable delivery of standards-conformant FHIR) and turning each solution into reusable frameworks the whole team builds on. This is an AI-native role: you'll pair first-principles engineering rigor with modern agentic tooling—coding agents such as Claude Code and reusable AI skills—to ship faster and turn raw health data into trustworthy, reasoning-ready health data intelligence. What You'll Do: - Work AI-Native & Set the Bar - Make coding agents (e.g., Claude Code) and reusable AI skills a first-class part of how the team engineers—shipping faster by automating the repetitive and amplifying the hard parts. - Bring the rigor to know when to trust an agent, when a deterministic rule-based approach wins, and how to keep AI-generated code correct, tested, and PHI-safe. - Package engineering and data-quality judgment into reusable tooling the whole team applies consistently. - Build & Scale Healthcare Data Solutions - Own the end-to-end architecture of large-scale pipelines that transform raw source data into clean, standards-conformant FHIR. - Diagnose and eliminate performance and scaling bottlenecks in distributed processing so jobs run faster and more cost-efficiently. - Set standards for data quality, de-duplication, patient/record linking, and terminology normalization; drive reliable, idempotent delivery at production volume. - Build automated testing and validation for data quality and FHIR conformance. - Lead, Secure & Mentor - Set technical direction across ingestion, transformation, and delivery, and define the frameworks and standards that accelerate the team. - Champion PHI-safe engineering and uphold HIPAA, 21st Century Cures Act, and ONC/CMS compliance across the platform. - Mentor senior and mid-level engineers, and represent b.well in relevant standards and industry discussions. Qualifications - 8+ years in data engineering, health IT, or healthcare interoperability—with a Staff-level track record of owning systems, not just tasks. - Proven production experience building large-scale health-data pipelines and owning a complex platform end-to-end, including its hardest performance and correctness problems. - Experience defining frameworks and standards that measurably accelerated a team. - AI-native engineer: you already ship with coding agents (e.g., Claude Code) and reusable AI skills, and can raise the whole team's leverage without compromising engineering rigor. Requirements - Distributed data engineering: Apache Spark / PySpark at scale (partitioning, performance and memory tuning); lakehouse tooling such as Databricks and Delta Lake; schema evolution; batch and streaming pipelines. - Pipelines & languages: ETL/ELT, CDC, incremental/delta and idempotent processing; strong Python (required) and SQL; Git, CI/CD, and automated testing. - FHIR & standards: HL7 FHIR R4 (resources, profiles, Bundles), US Core / USCDI, and HL7 v2.x / C-CDA to FHIR conversion. - Terminologies & payer data: SNOMED CT, LOINC, RxNorm, ICD-10, CPT, CVX and crosswalks; claims, coverage, and eligibility data models. - Compliance: HIPAA, 21st Century Cures Act, ONC certification, and CMS Interoperability Rule. Benefits - The target salary range for this position is $180,000 - $220,000 annually. - Part of a competitive total rewards package including stock options, benefits, and incentive pay for eligible roles. - Individual pay may vary from the target range and is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. - Annual review of employee pay and compensation programs to ensure competitive and fair pay. Company Description b.well is solving healthcare's fragmentation problem with our FHIR-based health data management platform. The platform connects data from EHRs, wearables, portals, and other sources, while our intelligence engine personalizes the consumer experience. By simplifying the complex healthcare ecosystem, we make it easy and convenient for consumers to engage and take action—whether it's scheduling care, setting reminders, accessing health data, and more. For our clients, this means better health outcomes, operational efficiency, and stronger consumer engagement.

Role Description Let's redefine cyber security expertise standards and connect business - community through highly engaging hacking experiences. ✨The core mission of the Senior Data Engineer: - Own and evolve data pipelines on GCP. - Build new pipelines, harden existing ones, and improve data quality. - Make clean, trustworthy data available across the organization. - Work end-to-end on streaming and batch pipelines. - Design ELT/ETL processes on BigQuery and ClickHouse. - Build real-time pipelines on Pub/Sub and Kafka with Dataflow. - Orchestrate workflows with Airflow. - Ensure data is properly cleaned, modeled, and served for analytics, ML training, and online inference. - Partner with ML engineers on feature pipelines and monitoring data drift. - Consume and build REST APIs and integrate with third-party SaaS sources. - Treat infrastructure as code. Qualifications - Strong data modelling and warehouse architecture skills. - Hands-on experience with GCP data services. - Production experience with streaming pipelines. - Solid SQL and strong Python skills. - Experience with ClickHouse or another columnar OLAP engine. - Workflow orchestration experience with Airflow. - Comfortable with dbt or equivalent transformation frameworks. - Experience migrating off legacy warehouses onto cloud-native stacks is a plus. - Working knowledge of ML in production. - Docker & Kubernetes experience. - CI/CD mindset and infrastructure-as-code sensibility. Requirements - Design and build batch and streaming pipelines on Dataflow, Pub/Sub, and Kafka. - Drive the migration off Snowflake onto GCP-native stack. - Own the orchestration layer in Airflow. - Model data for analytics and ML. - Partner with ML engineers on feature stores and retraining workflows. - Capture requirements from stakeholders and translate them into data products. - Continuously improve data quality, reliability, observability, and cost efficiency. - Identify new data sources worth acquiring and integrate them cleanly. Benefits - Compensation: $140,000-160,000. - Medical, Dental & Vision (employee coverage 100% paid). - 401K with employer match. - Employer-paid Life and AD&D Insurance. - Supplemental Life Insurance. - Short-term and Long-term Disability. - Healthcare and Dependent Care FSA. - Paid parental leave. - 25 annual leave days. - Home Office Allowance. - Dedicated budget for training and professional development. - State-of-the-art equipment. - Full access to the Hack The Box lab offerings.

• Design, build, and maintain scalable and efficient cloud-based data pipelines. • Develop and support ETL/ELT processes for data integration and transformation. • Ensure compliance with information security and data governance standards. • Maintain and enhance cloud data environments, including data lakes and data warehouses. • Collaborate closely with engineering teams, product teams, and stakeholders. • Contribute to technical research, continuous improvement, and adoption of new technologies. • Maintain code quality using version control systems and CI/CD practices. • Support the strategic evolution of data engineering solutions and infrastructure.