Data Engineer

Description Personetics is shaping the Cognitive Banking era, harnessing AI to help banks anticipate customer needs, provide actionable insights, and deliver intelligent financial guidance. Our platform continuously analyzes and leverages real-time transactional data, enabling banks to proactively support customers in managing their finances and reaching their goals. As industry leaders — yes, we really are leaders — we partner with the world’s top financial institutions, empowering over 150 million customers monthly across 35 global markets from offices in New York, London, Singapore, São Paulo, and Tel Aviv. About the position We are looking for an experienced and visionary Data Engineer to help design, build, and scale our BI platform. In this role, you will be responsible for developing our data analytics platform – enabling scalable data pipelines and robust data modeling to support real-time and batch analytics to provide insights that serve both business intelligence and product needs. You will be part of the R&D team, collaborating closely with engineers, analysts, and product managers to deliver a modern data architecture that supports internal dashboards and future-facing operational analytics. If you enjoy turning raw data into powerful insights and owning the full data lifecycle, this role is for you! Responsibilities Take full ownership of the design and implementation of a scalable and efficient BI data infrastructure, ensuring high performance, reliability, and security. Design and architect data products, from ingestion to transformation, modeling, storage, and access. Build and maintain ETL/ELT pipelines, batch and real-time, to support analytics, reporting, and product integrations. Establish and enforce best practices for data quality, lineage, observability, and governance to ensure accuracy and consistency. Integrate modern tools and frameworks such as Airflow, Databricks, Power BI, and streaming platforms. Collaborate cross-functional with product, engineering, and analytics teams to translate business needs into data infrastructure. Promote a data-driven culture – be an advocate for data-driven decision-making across the company by empowering stakeholders with reliable and self-service data access. Requirements 5+ years of hands-on experience in data engineering and in building data products for analytics and business intelligence. Experience working on data developments using AI tools and agentic workflows. Strong hands-on experience with ETL orchestration tools (Apache Airflow), and data lake houses (Databricks is an advantage) Vast knowledge in both batch processing and streaming processing (e.g., Kafka, Spark Streaming). Proficiency in Python, SQL, and cloud data engineering environments (AWS, Azure, or GCP). Familiarity with data visualization tools (Power BI, Looker, or similar). BSc in Computer Science or a related field from a leading university Nice to have Experience working on early-stage projects, building data systems from scratch. Background in building operational analytics pipelines, in which analytical data feeds real-time product business logic. Experience in cost optimization in modern cloud environments. Knowledge of data governance principles, compliance, and security best practices.