Senior Data Engineer
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Summary
Designs, builds, and maintains ETL/data pipelines into data warehouses using Python, SQL, Airflow, dbt, and streaming tools like Kafka, PySpark, or Dataflow on GCP/AWS/Azure, while modeling data and working with subscriber and audience-tracking datasets for a multinational technology and consulting company.
We are looking for experienced Data Engineers with strong Python, SQL and cloud experience. Join a multinational technology and consulting company and work on modern, scalable data solutions.
Key Job Responsibilities
- Design, develop, and maintain ETL/data pipelines from various sources into data warehouses.
- Design data models aligned with business needs for efficient querying and analysis.
- Ensure data orchestration pipelines are reliable, scalable, and efficient.
- Collaborate with data teams, vendors, and stakeholders to improve data products, processes, and systems.
- Follow data engineering best practices across coding, DevOps, documentation, and metadata.
- Work with audience tracking and subscriber datasets.
- Own assigned work, including development, testing, and delivery.
- Evaluate architectural and data modeling options, considering trade-offs and scalability.
- Maintain high-quality engineering output while meeting project timelines.
- Document current and future solution designs.
Key Job Requirements
- 5+ years of experience as a Data Engineer.
- Strong Python and SQL skills.
- Knowledge of OLTP/OLAP concepts and data modeling.
- Experience building data pipelines, ETL processes, and data infrastructure.
- Hands-on experience with Apache Airflow and DBT for workflow automation and transformations.
- Experience with Kafka/Pub/Sub, PySpark, or Dataflow.
- Strong analytical and problem-solving skills with excellent communication and collaboration.
- Understanding of cloud migration strategies and best practices.
- Experience with GCP, AWS, or Azure for data storage, processing, and analytics.
- Experience with cloud deployment and CI/CD/test automation.
- Familiarity with consumer behavior and/or subscriber data such as Google Analytics, Adobe Analytics, or Snowplow.
- Proficient use of AI tools to improve efficiency and focus on higher-impact technical work