Data Architect

1 week ago

Toronto, Ontario, Canada Tredence Inc. Full time

Databricks Architect

Job Summary

We are seeking a highly skilled Databricks Architect to design, implement, and optimize data solutions on the Databricks platform. The ideal candidate will have deep expertise in cloud-based data engineering, big data processing, and analytics, and a strong understanding of data architecture principles. You will play a pivotal role in delivering scalable, robust, and innovative data solutions that meet organizational goals.

Key Responsibilities

  • Design and develop end-to-end data architecture solutions on the Databricks platform.
  • Collaborate with stakeholders to understand business needs and translate them into technical requirements.
  • Lead the implementation of data pipelines and workflows using Databricks and other data engineering tools.
  • Optimize performance of Databricks notebooks, clusters, and jobs for efficiency and cost-effectiveness.
  • Integrate Databricks with cloud platforms (e.g., AWS, Azure, Google Cloud) and third-party tools.
  • Implement best practices for data governance, security, and compliance.
  • Drive the adoption of advanced analytics, machine learning, and AI use cases using Databricks.
  • Provide technical leadership, mentoring, and guidance to team members.
  • Monitor and troubleshoot data pipelines, ensuring high availability and reliability.
  • Keep up with the latest Databricks features and updates to recommend improvements.

Required Qualifications

  • Bachelor's or master's degree in computer science, Data Engineering, or a related field.
  • 10+ years of experience in big data engineering, architecture, or related field.
  • Strong expertise in Databricks, Spark, and big data technologies.
  • Hands-on experience with cloud platforms (AWS, Azure, or Google Cloud).
  • Proficiency in programming languages such as Python, Scala, or Java.
  • Solid understanding of data modeling, ETL processes, and data warehousing.
  • Experience with CI/CD pipelines and DevOps practices for data engineering.
  • Knowledge of data governance, privacy, and security principles.
  • Excellent problem-solving, communication, and teamwork skills.

Preferred Qualifications

  • Databricks certification (e.g., Databricks Certified Professional).
  • Familiarity with machine learning frameworks and libraries (e.g., MLflow, TensorFlow, PyTorch).
  • Experience with visualization tools such as Power BI, Tableau, or Looker.
  • Knowledge of Kubernetes and containerization for big data workloads.
  • Understanding of streaming data frameworks like Kafka or AWS Kinesis.
Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Consulting

Industries

Business Consulting and Services and IT Services and IT Consulting

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