Intermediate Data Engineer

Were looking for an authentic collaborative and accountableData Engineerto join the Arcurve AREPassionate about technologyAn authentic and creative humanDriven to succeedA believer in the importance of teamworkCommunity-mindedAn expert problem solverSomeone who thrives on challengeMotivated by exceptional resultsSomeone who cares about your clientsTHE GOALTo deliver best-in-class technical solutions across a broad array of clients in different industries utilizing the tech stack best suited to solving the problem with a focus on delivering business value for our ROLEArcurve delivers applied machine learning for clients operating in complex technical environments. The value of that work depends on whether it runs reliably against real operational data which is rarely clean complete or a Data Engineer you will build and operate the pipelines and platform that carry data from source to model to decision across batch and streaming workloads. You will also take a hands-on role in productionizing machine learning models working alongside a Data Scientist who owns the analytical position suits an engineer who thinks in terms of failure modes and who expects their systems to be inherited debugged and extended by other RESPONSIBILITIES Design build and maintain production data pipelines including ingestion transformation orchestration and data quality streaming and near-real-time pipelines where operational decisions depend on low MLOps infrastructure including CI/CD for models experiment tracking model registry deployment monitoring drift detection and validated machine learning approaches as production services that perform reliably under enterprise data models and semantic layers that support both analyst querying and reliable reasoning by large language and maintain cloud infrastructure and establish the observability alerting and recovery procedures that keep it with data scientists client engineering teams and business stakeholders on requirements and REQUIREMENTS Bachelors degree in Computer Science Engineering or a related technical field or equivalent practical experience building and operating production data -level SQL and strong experience with Databricks and/or Snowflake. Experience with BigQuery and Microsoft Fabric is also highly including performance tuning on production orchestration and workflow tooling such as Airflow Databricks Workflows Azure Data Factory or tooling such as MLflow Databricks Asset Bundles or Azure ML with practical experience deploying and monitoring delivering in a major cloud environment with Azure preferred and AWS a strong proficiency with Docker and Kubernetes including containerizing and deploying and infrastructure-as-code modelling experience across dimensional normalized or graph approaches with the judgment to select software engineering habits including version control code review automated testing structured logging and error written and verbal communication with both technical and non-technical QUALIFICATIONSStreaming platforms such as Kafka Event Hubs Kinesis or Spark Structured machine learning models at scale including real-time inference and GPU databases and graph data unstructured data at volume including images documents and governance lineage and exposure to industrial energy or engineering-led PERKS A fun work atmosphere that values equity diversity and contractor work environment and flexible or Employment opportunities