- Work model
- Not specified
- Experience
- Not specified
- Employment
- Not specified
- Compensation
- Not disclosed
- Technology signal
- 17 tags
Technology context
17Parsed from the vacancy text; ordered by relevance to this role.
AI
AWS
Azure
Python
Cloud
Reliability Engineering
Kafka
dbt
Scalability
Spark
Terraform
Databricks
CI/CD
Scala
Performance Optimization
SQL
AirflowFull listing
Role description
Hands-on senior engineer responsible for building, optimizing, and maintaining production data pipelines on one cloud platform, working within the broader data engineering team to deliver reliable, scalable data solutions that support analytics and downstream applications.
Key Responsibilities
- Design, build, and maintain scalable ETL/ELT pipelines on AWS or Azure
- Optimize pipeline performance, reliability, and cost-efficiency
- Implement data-quality checks, monitoring, and alerting for production pipelines
- Collaborate with the Data Engineering Lead and Architect on pipeline design and standards
- Support data modeling and schema design together with the Data Modelling Analyst
- Troubleshoot and resolve data pipeline issues in production
- Participate in code reviews and contribute to engineering best practices
Requirements
- 5+ years of hands-on data engineering experience
- Deep expertise in one cloud platform: AWS (Glue, S3, Redshift, EMR, Lambda, Kinesis) or Azure (Data Factory, Synapse, ADLS, Databricks)
- Strong SQL and Python (or Scala) skills; experience with orchestration tools (Airflow, ADF, etc.)
- Experience with ETL/ELT design, data-quality checks, and performance optimization
- Familiarity with distributed data processing frameworks (Spark)
- Experience operating production data pipelines at scale
- Solid understanding of data warehousing and dimensional modeling concepts
Nice to Have
- Familiarity with CI/CD and infrastructure-as-code (Terraform)
- Experience with streaming data pipelines (Kafka, Kinesis, Event Hubs)
- Exposure to dbt or similar transformation tooling