data engineer in data platforms

and GlueProcess and transform large datasets using Spark and FlinkDesign production-ready systemsWork across relational, NoSQL, and analytical data storesOptimise storage formats and access patternsImplement secure, compliant data solutions with security by designEmbed governance while maintaining developer velocityWork directly with clients to understand problems and shape solutionsTranslate business needs into pragmatic engineering decisionsAct as a trusted technical advisorSet engineering standards, patterns, and best practices across teamsReview designs and code, providing technical direction and mentorshipImprove data quality, testing, observability, and operational excellenceТребованияStrong Python and SQL skillsDeep experience with Spark and modern data platforms such as Databricks and SnowflakeSolid understanding of cloud data services in AWS or GCPDemonstrated ownership of large-scale data platform architecturesStrong data modelling and architectural decision-making skillsAbility

to balance performance, cost, and complexity trade-offsExperience building and operating large-scale data pipelines in productionExperience with multiple storage technologies and formatsInfrastructure-as-code experience with Terraform or PulumiExperience with CI/CD pipelines using tools such as GitHub Actions or ArgoCDExperience with data testing and quality frameworks such as dbt, Great Expectations, or SodaExperience in consulting or professional services environmentsStrong consulting instincts and ability to challenge assumptions and guide clients toward better outcomesAbility to mentor senior engineers and influence technical cultureУсловияNo conditions specified #J-18808-Ljbffr

Описание: Simple Machines is a global, independent technology consultancy that designs and builds modern data platforms, intelligent systems, and bespoke software across data engineering, software engineering, and AI. It helps enterprises, scale-ups, and government turn complex data into products, platforms, and actionable decisions.ЗадачиOwn the end-to-end architecture of modern, cloud-native data platformsDesign scalable data ecosystems using data mesh, data products, and data contractsMake architectural decisions across ingestion, storage, processing, and access layersEnsure platforms are secure, compliant, and production-grade by designDesign and deliver cloud-native data platforms using Databricks, Snowflake, AWS, and GCPIntegrate with client systems to enable scalable, consumer-oriented data accessBuild and optimise batch and real-time pipelinesWork with streaming and event-driven technologies such as Kafka, Flink, Kinesis, and Pub/SubOrchestrate workflows using Airflow, Dataflow