Data Engineer
RomaniaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 4+ years
- Required Skills
- AWSPythonSQLGitSnowflakeAirflowTerraformData modelingdbt
Requirements
- 4+ years of professional experience designing, developing, and deploying production-grade data pipelines using Python.
- Strong expertise in SQL development, including complex queries, optimization techniques, and performance tuning in cloud data environments.
- Hands-on experience with Snowflake, including data loading, external stages, security controls, warehouses, and optimization practices.
- Strong experience with dbt Core for data transformation, testing, incremental models, and analytics engineering workflows.
- Solid understanding of data warehouse design principles, including dimensional modeling, star and snowflake schemas, facts, dimensions, and slowly changing dimensions.
- Experience designing conceptual, logical, and physical data models.
- Proven experience building and operating ETL/ELT pipelines using Airflow or similar orchestration tools.
- Familiarity with modern AI-assisted development workflows and daily use of AI coding tools for development, debugging, and code review.
- Knowledge of AWS services such as S3, Lambda, IAM, and EventBridge, along with Terraform and CI/CD practices.
- Experience with Git, Docker, data quality frameworks, and data lineage practices is highly valued.
- Strong problem-solving skills, attention to detail, and ability to work effectively in a remote, collaborative engineering environment.
Responsibilities
- Build and maintain robust data ingestion pipelines from various sources, including REST APIs, SFTP, and database replication systems into cloud data platforms.
- Develop and optimize data transformation workflows using dbt, following structured bronze, silver, and gold layer modeling practices.
- Design and maintain data models supporting identity resolution, customer segmentation, attribution, and marketing activation use cases.
- Build integrations with external marketing platforms and services, enabling data synchronization, campaign delivery, and feedback collection.
- Develop and manage AWS Lambda functions, Airflow workflows, and orchestration processes to ensure reliable data operations.
- Implement data quality checks, monitoring, alerting, and validation frameworks to maintain accuracy and trust in business-critical datasets.
- Manage cloud infrastructure and data platform components using infrastructure-as-code practices with Terraform.
- Investigate and resolve data incidents, including pipeline failures, quality issues, and sensitive data exposure risks.
- Contribute to continuous improvement of engineering practices, automation, documentation, and scalable data architecture.
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