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Responsibilities
- Build and maintain end-to-end data pipelines in AWS, often ingesting data from multiple REST APIs.
- Design reliable, observable, and scalable pipelines, with a strong focus on data quality and monitoring.
Explain and discuss the logic of your solutions with stakeholders, not just the tools used.
Requirements
Advanced English, comfortable explaining technical solutions in conversation.
Proven experience as a Data Engineer
- Strong hands-on experience in Python (data processing, API integration, automation) and advanced SQL (CTEs, window functions, optimization).
- Solid experience designing, building, and maintaining data pipelines on AWS, including:
- Ingestion from REST APIs (authentication, pagination, rate limiting, error handling).
- Transformation and loading into data lakes / data warehouses.
- Orchestration (e.g. Step Functions, Glue Workflows, or similar).
- Strong hands-on experience with AWS Serverless and data ecosystem:
- Lambda, Glue (Glue Jobs / Glue Studio), DynamoDB, ECS/Fargate (if applicable).
- S3, IAM, CloudWatch (logs, metrics, alarms), Step Functions.
- Experience with PySpark or similar distributed processing frameworks (e.g. Spark on EMR/Glue).
Nice to have
- Experience in marketing data use cases (campaigns, events, customer 360, CDP).
- Experience with Adobe Experience Platform (AEP), especially CDP.
- Experience with event-based architectures (e.g. tracking events, customer journeys).
- Good understanding of Data Engineering fundamentals:
- Data modeling (dimensional and relational), data structures.
- ETL vs ELT, batch vs streaming workflows.
- Data quality, data governance, and data observability practices (monitoring, alerting, lineage).
- Familiarity with Git/GitHub and CI/CD workflows for data pipelines.
- Strong problem-solving mindset: Ability to clearly explain the logic behind the solution, trade-offs and troubleshooting steps.
- Strong communication skills and ability to collaborate with cross-functional teams.
#LI-AG4
#mid-senior
