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Personetics Data Engineer - BI Infrastructure Israel, Tel Aviv District, Tel Aviv-Yafo 23417957
Yesterday
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Responsibilities Requirements Nice to have
- Data Strategy & Architecture – Design and implement a scalable and efficient BI data infrastructure, ensuring high performance, reliability, and security.
- Data Integration & Pipeline Development – Build and maintain automated data pipelines (ETL/ELT) that integrate internal and external data sources into a unified cloud-based data warehouse.
- Data Quality & Governance – Establish best practices for data governance, lineage, monitoring, and compliance, ensuring accuracy, consistency, and security.
- Collaboration & Cross-Team Impact – Partner with Data Science, BI, Product, and Business teams to deliver high-value insights, supporting analytics, ML models, and reporting.
- Technology Leadership – Work with modern data stack tools such as Python, DBT, Databricks, Spark, Looker, Airflow, Kubernetes, and Azure to build a best-in-class BI ecosystem.
- Data-Driven Culture – Advocate for data-driven decision-making across the company by empowering stakeholders with reliable and self-service data access.
- Experience: 4-5 years of hands-on experience in Data Engineering, Business Intelligence, or a related field, with a proven track record of designing and managing scalable data infrastructure.
- Data Warehousing: Expertise in at least one designing and managing cloud-based data warehouses (e.g., Snowflake, Databricks, BigQuery, Redshift).
- Strong SQL and Python skills.
- Experience with at least BI tool (Looker, Tableau, Power BI).
- Proficiency in ETL/ELT frameworks (Airflow, DBT, etc.).
- Familiarity with big data processing tools (Spark, Databricks).
- Experience with cloud platforms (Azure, AWS, or GCP).
- Ability to work independently in a fast-paced environment.
- Excellent communication skills, with the ability to translate complex data concepts for non-technical stakeholders.
- Strong problem-solving skills and a passion for building scalable, high-impact data solutions.
- Experience in Fintech, SaaS, or data-driven B2B2C environments.
- Knowledge of data governance principles, compliance, and security best practices.
- Hands-on experience with ML model training pipelines.
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