Senior Data Engineer

2 weeks ago

Bucharest, Bucureşti, Romania Luxoft Full time €60,000 - €80,000 per year

Project description

Our client is a leading global Financial markets data provider runs a number of change programs to deliver of high-quality software that connects Financial Markets across the globe in a real-time, high frequency and low-latency data management chain. The projects are technically challenging in a very engaging environment making the most of cutting-edge technologies.

Responsibilities

  • Design, build, and maintain scalable ETL/ELT data pipelines to support analytics, reporting, and machine learning use cases.
  • Develop and optimize data models and warehouse structures in Snowflake to ensure high performance and reliability.
  • Collaborate with data analysts, data scientists, and business stakeholders to understand requirements and deliver clean, reliable, and accessible datasets.
  • Implement and manage data orchestration workflows (e.g., Airflow, AWS Step Functions) to ensure timely and accurate data delivery.
  • Apply best practices in data governance, quality, and security across all pipelines and storage layers.
  • Monitor, troubleshoot, and optimize data processes for performance, scalability, and cost efficiency.
  • Leverage AWS cloud services to design resilient, scalable, and secure data architectures.
  • Use Python and SQL for advanced data transformations, automation, and performance optimization.
  • Document data pipelines, models, and workflows to ensure maintainability and transparency.
  • Contribute to the continuous improvement of the data engineering platform, adopting new tools, frameworks, and processes.

SKILLS

Must have

  • Strong proficiency in Python for ETL/ELT development and data pipeline automation.
  • Hands-on experience with AWS services (S3, Glue, Lambda, EMR, RDS) for building and managing scalable data solutions.
  • Expertise in Snowflake for cloud data warehousing, performance tuning, and large-scale data management.
  • Advanced SQL skills for data modeling, transformations, and query optimization.
  • Practical experience with dbt for modular, version-controlled data transformations and modeling.

Nice to have


• Experience with data orchestration tools (Airflow, Prefect, AWS Step Functions).
• Familiarity with big data frameworks (Apache Spark, Kafka, Kinesis).
• Knowledge of CI/CD pipelines and version control (GitHub/GitLab, Jenkins).
• Understanding of data modeling techniques (star/snowflake schemas, data lakehouse).
• Exposure to monitoring & observability tools (CloudWatch, Datadog, Prometheus).
• Awareness of containerization and deployment (Docker, Kubernetes).

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