Data Engineering Lead

7 hours ago

Stockholm, Stockholm, Sweden Rasulson Consulting AB Full time

Rasulson Consulting AB is seeking a highly skilled Data Engineering Lead to spearhead the design, development, and implementation of robust data pipelines and AI-driven solutions. The ideal candidate will have extensive experience in cloud architecture, machine learning pipelines, and data engineering.

Job Description:
  • Data Engineering & Solution Development
    • Design and implement scalable data pipelines using tools such as BigQuery, DBT, and advanced SQL optimization techniques.
    • Develop real-time data streaming solutions with technologies like Kafka, Pub/Sub, and scheduling tools such as Cloud Composer (Apache Airflow).
    • Build and maintain ETL workflows, data lakes, and data warehouses to support both analytical and AI applications.
    • Utilize Terraform to implement secure, scalable, and automated infrastructure solutions.
    • Ensure proper data governance, lineage, and access controls through the use of IAM policies and Data Catalog tools.
  • MLOps & AI-Driven Solutions
    • Develop and optimize comprehensive machine learning pipelines using platforms such as Vertex AI Pipelines, Kubeflow, and Dataflow.
    • Streamline processes such as feature engineering, model deployment, and monitoring in production environments.
    • Work with cutting-edge AI methodologies including prompt engineering, generative AI (GenAI), Retrieval-Augmented Generation (RAGs), and agentic workflows.
    • Partner with Data Scientists to fine-tune model performance and ensure smooth, scalable integration into production systems.
  • DevOps & Software Engineering
    • D deliver high-quality software using Python—leveraging object-oriented programming, unit testing (with Pytest/Unittest), and tools like flake8, mypy, black, and SonarQube.
    • Design and maintain continuous integration and delivery pipelines using GitHub Actions, Azure DevOps, and Docker containerization.
    • Implement GitOps methodologies to facilitate seamless collaboration, effective version control, and automated deployments.
  • Stakeholder Collaboration
    • Translate complex business needs into practical technical solutions, ensuring strategic alignment across departments.
    • Work closely with teams across Data Science, AI, and other business functions to optimize data workflows and system integration.
    • Contribute to the team's technical expertise by leading workshops, presentations, and maintaining clear, accessible documentation.
  • Dashboarding & Data Visualization
    • Create dynamic dashboards using tools like Power BI, Tableau, or Looker to deliver real-time insights.
    • Ensure seamless connectivity between cloud-based databases (e.g., BigQuery) and BI tools for effective data visualization.
    • Apply industry-standard techniques in data modeling and visualization to generate meaningful business insights.
  • Documentation & Continuous Improvement
    • Maintain clear and structured documentation for all architectural designs, data workflows, and engineering decisions.
    • Regularly conduct code reviews, performance audits, and optimizations to enhance system efficiency and scalability.
    • Stay abreast of emerging trends in AI, MLOps, and cloud engineering to continuously improve our data infrastructure and processes.
    Requirements:
    • A bachelor's or master's degree in Computer Science, Data Science, or a related field.
    • At least 5 years of experience in data engineering, cloud architecture, and developing machine learning pipelines.
    • Proven proficiency with Google Cloud Platform (GCP) technologies, including BigQuery, Pub/Sub, Dataflow, Cloud Composer, Vertex AI Pipelines, and Kubeflow.
    • Strong command of Python and SQL, with hands-on experience in DevOps tools, Terraform, Unix-based systems, and shell scripting.
    • Experience with feature engineering, model deployment, AI prompt engineering, generative AI, and Retrieval-Augmented Generation (RAGs).
    • Skilled in developing dashboards and performing data modeling using tools like Power BI, Tableau, or Looker.

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