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Keep IT Simple
Keep IT Simple
11 – 50 Darbinieki
KiberdrošībaLoģistikaVeselības aprūpe
Keep IT Simple (KIS) ir Silīcija ielejā bāzēts IT pakalpojumu un risinājumu sniedzējs, kas apkalpo organizācijas visā Kalifornijā un ārpus tās. Uzņēmums, kas dibināts 1988. gadā, palīdz klientiem risināt sarežģītas tehnoloģiju vajadzības, piedāvājot kiberdrošības, virtualizācijas, mākoņrisinājumu un tīkla infrastruktūras konsultācijas. Tā darbība aptver tādas nozares kā loģistika, veselības aprūpe un kiberdrošība, īpašu uzmanību pievēršot praktiskiem IT ieteikumiem, izmaksu ziņā pārdomātiem risinājumiem un atsaucīgai klientu apkalpošanai.
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Vakances uzņēmumā

Senior Data Engineer, Databricks and AI Data Platforms

Lead the design of Databricks data platforms for a global insurance and asset management company. Enable cloud analytics, AI and machine learning, governance, and enterprise modernization.

BRBrazīlijaHibrīdiPilna slodzeVecākais speciālistsNepieciešamā Valoda: angļuDatu inženieris
ApacheAWSAzureETLKafkaMākonisOraclePostgresPySparkPythonSparkSQLTerraformUnity

Apraksts

  • Design and maintain scalable data pipelines with Databricks, Spark, and cloud technologies.
  • Build and optimize batch and real-time data ingestion frameworks.
  • Create reusable engineering patterns and accelerators for enterprise data solutions.
  • Implement data quality, observability, lineage, and governance across the data ecosystem.
  • Support large-scale data modernization and cloud migration initiatives.
  • Design Delta Lake architectures and Medallion data models.
  • Develop optimized ETL and ELT pipelines using Databricks notebooks, workflows, and Delta Live Tables.
  • Set performance tuning, cost optimization, and workload management practices.
  • Use Unity Catalog to manage data governance, security, and metadata.
  • Implement CI/CD pipelines and infrastructure as code for Databricks deployments.
  • Design cloud-native data architectures for Azure and AWS.
  • Integrate enterprise sources including SQL Server, Oracle, APIs, SaaS, and streaming platforms.
  • Build resilient, secure data services that meet high availability and disaster recovery needs.
  • Work with infrastructure and security teams to apply cloud security controls and compliance standards.
  • Build and optimize data products for AI, machine learning, and generative AI workloads.
  • Develop feature engineering pipelines and curated datasets for model training and inference.
  • Support vector databases, semantic search, retrieval-augmented generation, and LLM applications.
  • Partner with data scientists and AI engineers to put machine learning solutions into operation.
  • Apply MLOps and DataOps practices to support scalable AI delivery.
  • Lead complex data engineering initiatives.
  • Set architecture standards, engineering practices, and development guidelines.
  • Mentor junior and mid-level engineers.
  • Review architectures and provide technical recommendations.
  • Promote automation, observability, and platform engineering practices.

Prasības

  • Bachelor’s degree in computer science, information technology, engineering, or a related field.
  • At least 8 years of experience in data engineering, data warehousing, or data platform development.
  • At least 5 years of hands-on Databricks and Apache Spark experience.
  • Experience designing and implementing enterprise-scale cloud data platforms.
  • Experience leading technical initiatives and mentoring engineering teams.
  • Experience with Databricks, Apache Spark (PySpark and Spark SQL), Delta Lake, Delta Live Tables, Unity Catalog, data warehousing, and data modeling.
  • Experience with Python, SQL, and PowerShell or scripting automation.
  • Experience with Microsoft Azure, AWS, and cloud-native storage and compute services.
  • Experience with SQL Server, Oracle, PostgreSQL, and databases.
  • Experience with Azure Data Factory, Databricks Workflows, Kafka or event streaming, and REST APIs.
  • Experience with GitHub, Azure DevOps, CI/CD pipelines, Terraform, and infrastructure as code.
  • Knowledge of monitoring and alerting tools, data quality frameworks, performance optimization, logging, and troubleshooting.
  • Knowledge of machine learning data pipelines, feature stores, MLOps, generative AI concepts, vector databases, retrieval-augmented generation, AI governance, and responsible AI principles.
  • Preferred certifications: Databricks Certified Data Engineer Professional, Azure Data Engineer Associate, or AWS Data Analytics.
  • Preferred experience in regulated industries, enterprise data governance programs, and production AI integrations.
  • Strategic thinking, problem-solving, technical leadership, architecture design, stakeholder management, communication and presentation skills, continuous improvement, innovation and AI adoption, and risk and compliance awareness.

Priekšrocības

  • Contracting arrangement: independent contractor (Pessoa Jurídica, PJ).
  • Hybrid working arrangement with the option to work remotely part of the time.

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