Data Architect

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 a Senior Data Architect to help define and evolve the data architecture of our SaaS platform and core products. You will work closely with product, delivery, and engineering squads to design scalable, reliable data systems, set technical direction for how data is ingested, modeled, stored, and served, and guide teams in making high-quality architectural decisions that balance delivery speed with long-term sustainability. This is a hands-on, senior technical leadership role: you will spend most of your time shaping data architectures with teams, reviewing data models, pipelines and critical code, and driving shared standards and patterns across the organization, all in an advanced agentic environment. Responsibilities Define and evolve the data architecture for key domains (ingestion, ELT/ETL, data modeling, storage, APIs, integration, observability, governance, and security), including owning the lakehouse/medallion architecture (bronze/silver/gold) and the data flows that move data across layers at scale. Translate business and product requirements into pragmatic data designs, data contracts, and architecture roadmaps; create and maintain architecture artefacts (data flow/lineage diagrams, ADRs, reference implementations, modeling guidelines). Evaluate design options and technology choices, articulate trade-offs, and lead decision-making with stakeholders; push forward the agentic mindset and implementation across the data platform. Partner with squad leads and senior engineers to design data solutions, break down complex problems, and keep implementations aligned with the target architecture; participate in design/tech reviews to ensure NFRs (performance, scalability, data quality, resilience, security, operability) are addressed early. Provide hands-on support where it matters most: spike and prototype critical data flows, review complex PRs, and help debug tricky production data and pipeline issues. Collaborate with Product to shape technical feasibility, sequencing, and scope for data-driven roadmap items; communicate complex technical topics in simple language to non-technical stakeholders. Define and promote data architecture principles, modeling conventions, coding standards, and reusable patterns (shared transformations, libraries, datasets, services) to reduce duplication and technical debt; drive adoption of shared platform capabilities (observability, CI/CD, orchestration, governance, DevOps tooling) across squads. Ensure data architectures are observable, operable, and resilient by design; partner with DevOps/SRE on deployment, monitoring, data quality, and incident response; identify areas of high technical debt or architectural risk and lead remediation initiatives; define and track technical KPIs tied to architecture decisions. Act as a technical mentor for senior engineers and tech leads, fostering a culture of thoughtful design, documentation, and constructive technical debate; lead by influence- help teams make better decisions instead of making every decision for them. Requirements 8+ years of experience in software / data engineering, including several years in a senior / staff / architect role designing complex data systems. Strong experience designing modern data platforms and distributed data architectures (lakehouse/warehouse, batch and streaming/event-driven patterns, robust data APIs). Experience working with columnar/serialization data formats such as AVRO and Parquet, including schema evolution and storage trade-offs. Experience with DBT and ELT management tools for building, testing, and maintaining transformation pipelines. Experience with Apache Airflow (or comparable orchestration tooling) for scheduling and managing data workflows. Experience working with Databricks (or Snowflake) and medallion architecture (bronze/silver/gold). Experience building SaaS data infrastructure, including CI/CD, ETL/data pipeline observability, and data quality monitoring. Proven ability to design for scale, performance, security, and reliability in production SaaS environments. Hands-on experience with at least one major language and ecosystem used in our stack (e.g., Python, SQL, Java, or similar). Proven agentic experience – as hands-on experience and as architecting GenAI systems. Comfortable reading and reviewing code, guiding implementation, and occasionally building prototypes or reference implementations. Nice to have Experience with financial services, B2B SaaS, or integrations with large enterprise customers (e.g., banks). Background in analytics, BI, or ML-adjacent systems and feature/data pipelines for ML. Familiarity with data governance, lineage, cataloging, and master data management. Familiarity with domain-driven design, event sourcing, or CQRS patterns. Solid understanding of cloud-native architecture (e.g., AWS/Azure/GCP), containers, and infrastructure-as-code practices.