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Data Engineering Team Lead
לא צויןמשרה מלאהData 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.
תחומי אחריות
- 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.
דרישות
- 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.
יתרון
- 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).