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Data Engineering Team Lead
·Israel
Not specifiedFull-timeData EngineeringBeautyWellness
Hands-on team lead responsible for designing and scaling data platform and pipelines. Leads small team while remaining technical, owning delivery and architecture. Requires strong SQL, Python, AWS data services experience.
Responsibilities
- Lead data engineering team delivering reliable, scalable data pipelines and data models.
- Design, build, and maintain ETL/ELT pipelines.
- Own data workflows and scheduling using AWS MWAA (Managed Airflow), including orchestration and migration jobs.
- Develop and optimize data processing pipelines using AWS Glue.
- Build robust data transformations using Python and SQL.
- Work with large-scale distributed processing using PySpark.
- Manage and optimize data warehouse structures in Amazon Redshift.
- Ensure data quality, consistency, and performance across pipelines and datasets.
- Collaborate closely with Product, Marketing, and Data Analytics teams to translate business requirements into data solutions.
- Support BI use cases and verify data availability for reporting in Looker.
- Manage work tracking, sprint planning, and delivery using Jira.
- Participate in architecture discussions and drive best practices across the data.
- Be in charge and available and make sure our system is 24/7 online.
Requirements
- 3+ years of experience in data engineering.
- Strong SQL skills (critical requirement).
- Strong programming skills in Python.
- Experience building ETL/ELT pipelines.
- Experience with AWS data services, especially AWS Glue, AWS MWAA (Airflow), Amazon Redshift.
- Experience working with data warehouses and large datasets.
- Familiarity with BI tools, preferably Looker.
- Experience working in agile environments using Jira.
Nice to have
- Experience with PySpark.
- Experience working directly with marketing/product analytics teams.
- Understanding of data modeling for analytics and BI layers.
- Experience leading small engineering teams or mentoring engineers.
- Exposure to cloud data architecture best practices (scalability, cost optimization, governance).