• Gather and document requirements from internal stakeholders • Define, track, and report on key performance indicators (KPIs) • Translate business questions into data models, queries, and dashboards • Prepare clear reports and present findings to technical and non-technical audiences • Help build and maintain ELT pipelines from Segment into Snowflake • Monitor and validate event data flowing through Segment — checking completeness, consistency, and correct destination mapping • Assist with data quality audits across the Segment → Snowflake → Looker stack • Investigate and document data anomalies and pipeline failures • Support data model development and maintenance in Snowflake • Build dashboards and reports in Looker • Write and optimize SQL queries against Snowflake • Assist with basic LookML development: views, explores, and measures • Apply data visualization best practices for clarity and audience fit • Use AI assistants (Claude, Copilot, ChatGPT) to speed up SQL writing, documentation, and analysis • Apply text-to-SQL and LLM-based tools for faster data exploration • Prototype basic AI features: anomaly detection and automated report summarization • Contribute ideas for AI and automation adoption within the team • Track tasks and participate in Agile sprints using Jira • Use Git/GitHub for version control of SQL, LookML, and Python scripts • Participate in code and dashboard reviews • Document data models, event schemas, metric definitions, and pipeline logic

• Design and implement solutions to meet specific needs of the program stakeholders, including workflows, databases, and data visualizations using Power BI and other Microsoft Power Platform tools. • Evaluate and normalize datasets to be used as the sources of dashboards. • Recommend updates that need to be made to clean up datasets as needed. • Perform hands-on Power BI development tasks and implementations. • Make updates to existing Power BI dashboards and Power Platform apps based on stakeholder requests. • Support system integrations between various applications, such as Power BI, SharePoint, and Salesforce. • Provide development support, from design through testing and deployment.

• Analyze and understand data sources and design data models for capturing structured and unstructured data. • Perform statistical modeling, quantitative analysis, and data mining to extract valuable insights. • Develop and deliver Key Performance Indicators (KPIs) for various organizational needs in consultation with stakeholders. • Utilize data visualization tools to create reports and dashboards that communicate insights effectively. • Participate in Data Governance initiatives to establish data quality standards and measures. • Ensure the highest standards of data collection, recording, analysis, maintenance, storage, adaptation, retrieval, and transmission from various data sources. • Support the training and development of Data Stewards within various departments to enhance data analysis and visualization capabilities. • Stay updated with various Data Architectures to support diverse data needs of the organization. • Monitor and assess system performance for continuous improvements. • Collaborate with the Information Technology team to ensure the seamless integration of data tools and resources. • Provide technical assistance and direction to other Analysts for the purposes of resolving complex issues. • Provide direction in adoption of modern Data technologies as per the needs and demands of the organization.

Role Description We are looking for a Data Analyst for our Risk AI Platform - the group that owns the data and ML infrastructure behind Plata's credit decisioning. At the center of that infrastructure is our feature store: the system that turns raw external and internal data into the features that power our scoring models, both in research and in real-time production. The role sits at the intersection of data engineering, analytics, and ML: you'll spend most of your time in SQL, dbt, Python, and Snowflake, building reliable pipelines and making sure the features our models consume are correct, consistent, and well-monitored - offline and online. Challenges that await you: - Develop and maintain the feature store: design and optimize dbt models across our multi-layer pipeline, from raw ingestion through parsing, unification, and domain modeling to production-ready feature sets. - Onboard new external data sources (credit bureaus, alternative data, identity and scoring providers) into the platform — from raw parsing to production features. - Guarantee consistency between training and serving: build checks and monitoring that catch feature drift, mismatches, and data-quality issues before they reach a model. - Own data quality and observability for the features you ship — freshness, completeness, lineage, and reconciliation. - Work closely with data scientists and ML engineers to productionize features for scoring models, and help turn research feature logic into robust, tested pipeline code. - Improve our tooling, standards, and documentation so the whole team ships features faster and more safely. Qualifications - 2-3 years of experience in a data engineering, analytics engineering, or strongly engineering-oriented data analyst role. - Strong SQL: you're comfortable writing and optimizing complex queries. - Solid Python for data work (data pipelines, scripting, pandas). - Hands-on experience with dbt, or with building ETL/ELT pipelines you can map onto dbt. - A working understanding of ML — enough to reason about features, training/serving consistency, and how your pipelines feed a model. - A data-quality mindset: you care about correctness, reproducibility, and monitoring, not just getting the number out. - B1 or higher English level for effective communication with an international team. Requirements - Background in Risk, credit, lending, or fintech. - Experience with Snowflake (or a comparable cloud data warehouse) and a cloud platform (AWS). - Familiarity with feature stores, ML model registries. - Comfort with A/B testing and applied statistics. - Experience with orchestration and CI/CD for data. Benefits - Relocation support to one of our hubs — Cyprus, Serbia, Georgia or Kazakhstan — with assistance for the employee and their family. - Flexible work from one of our offices or remote. - Healthcare Coverage. - Education Budget: Language lessons, professional training and certifications. - Wellness Budget: Mental health and fitness activity reimbursements. - Vacation policy: 20 days of annual leave and paid sick leave.