Data Engineer (AWS)
New
R
RITS GroupData engineering
Locations: Warszawa, Żelazna, Warszawa, Country code: PL; Tryb usługi: 100% zdalnieContractMiddle
Salary140 - 160 PLN per hour
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Job Details
- Languages
- Polish B2; English B2
- Experience
- Minimum 4 lata doświadczenia w obszarze Data Engineering
- Required Skills
- AWSPythonSQLCI/CDdbtPySpark
Requirements
- Have at least 4 years of data engineering experience.
- Have independently developed and maintained a production data warehouse.
- Have strong SQL and Python skills, including PySpark.
- Have practical experience with Amazon Redshift or a similar MPP solution, including performance optimization, distribution keys, sort keys, and workload management.
- Know data modeling, including dimensional modeling and Slowly Changing Dimensions.
- Have experience with dbt incremental models, tests, and sources.
- Have practical experience with Segment or a similar Customer Data Platform and event schema design.
- Have AWS experience with S3, IAM, Glue, Lambda, Step Functions, Spark on EMR, and RDS/Aurora.
- Know infrastructure as code, such as CloudFormation, AWS SAM, or Terraform, and have CI/CD experience.
- Be able to design idempotent pipelines and data reprocessing/backfilling processes.
- Have practical experience preparing data for ML/AI, such as feature engineering, training datasets, or model serving.
- Have English proficiency at B2, C1, or C2 level.
Responsibilities
- Develop and maintain data pipelines and respond to operational issues.
- Design data ingestion processes, including integrations with Aurora PostgreSQL and SQS.
- Build and optimize data transformations using PySpark and dbt.
- Improve data quality controls, freshness monitoring, and pipeline reliability.
- Manage Segment configuration and design event schemas with Product and Engineering teams.
- Maintain data consistency between Redshift and Braze, Amplitude, AppsFlyer, and Looker.
- Prepare and provide data to external tools, partners, and internal teams.
- Develop ML models, feature pipelines, and training datasets, and deploy models with AWS SageMaker; support AI and LLM initiatives.
- Optimize Amazon Redshift performance and costs, and develop infrastructure as code and CI/CD.
- Modernize architecture, standardize business metrics with Analytics, Finance, and Product, review code, and support less experienced team members.
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