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Keep IT Simple
Keep IT Simple
11 – 50 Zaposleni
Kibernetska varnostLogistikaZdravstveno varstvo
Keep IT Simple (KIS) je ponudnik IT-storitev in rešitev s sedežem v Silicijevi dolini, ki oskrbuje organizacije po vsej Kaliforniji in širše. Podjetje, ustanovljeno leta 1988, strankam pomaga pri reševanju kompleksnih tehnoloških potreb na področju kibernetske varnosti, virtualizacije, rešitev v oblaku in svetovanja o omrežni infrastrukturi. Njegovo delo zajema panoge, kot so logistika, zdravstvo in kibernetska varnost, pri čemer poudarja praktične IT-usmeritve, stroškovno premišljene rešitve ter odzivno podporo strankam.
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Delovna mesta v podjetju

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.

BRBrazilijaHibridnoPolni delovni časSeniorZahtevani Jezik: angleščinaPodatkovni inženir
ApacheAWSAzureETLKafkaOblakOraclePostgresPySparkPythonSparkSQLTerraformUnity

Opis

  • 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.

Zahteve

  • 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.

Ugodnosti

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

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