• Partner with trading partners to understand business and data integration requirements during implementation and onboarding. • Analyze incoming customer data and perform gap analysis to identify discrepancies, missing information, and opportunities for improvement. • Configure and implement customer integrations using existing proprietary data mapping and transformation requirements. • Communicate integration specifications to customers and business partners, recommending enhancements to file layouts, naming conventions, and data structures when needed. • Collaborate closely with Software Development and Product teams to support enhancements to existing integrations and implement new capabilities. • Validate imported data to ensure accuracy, completeness, and successful processing. • Monitor daily automated data integrations across more than 460 business partners, proactively identifying and resolving data issues. • Investigate and troubleshoot data integration failures, working directly with internal teams and external partners to drive timely resolution. • Support internal and external customer inquiries related to data transformation, file processing, and integration workflows. • Prioritize multiple implementation projects and support requests while meeting established business deadlines. • Contribute to continuous improvement initiatives that enhance integration processes, data quality, and customer experience.

Role Description Our client, a fast-growing digital technology service provider, is seeking an experienced Senior Business Analyst to join a strategic market data transformation programme for a global financial services organisation. This role offers the opportunity to modernise complex legacy systems and shape the future of market data platforms. - Lead a strategic market data transformation for a global financial services organisation - Work on a large-scale legacy platform modernisation using Microsoft cloud architecture - Collaborate with engineering teams, architects and stakeholders across multiple locations - Challenge existing processes and drive meaningful improvements Responsibilities - Analyse legacy market data platforms and document workflows - Define future-state requirements with stakeholders and architects - Challenge processes to identify simplification and improvement opportunities - Translate requirements into clear user stories and specifications - Support migration to modern cloud-based data architecture - Collaborate with development teams throughout delivery lifecycle - Identify and escalate delivery risks, dependencies and gaps - Facilitate workshops with technical and business stakeholders - Support Agile teams through refinement, planning and acceptance Qualifications - Significant experience as Business Analyst in large-scale transformation programmes - Strong experience with data-intensive platforms and complex processing environments - Experience analysing large-scale ETL or data integration solutions - Experience working within Agile delivery teams - Demonstrated ability to work independently with minimal supervision - Experience working directly with engineering and architecture teams Domain Knowledge - Experience in Financial Services, Capital Markets, Market Data, Investment Banking or Exchanges - Strong understanding of financial instruments, pricing, quotes, volumes and time series data - Knowledge of commodities, equities, fixed income or derivatives - Experience with market data providers such as Bloomberg, LSEG, S&P Global, FactSet or ICE (advantageous) Technical Knowledge - Strong conceptual understanding of ETL pipelines and data integration - Knowledge of data processing platforms and low-latency processing - Understanding of data quality, validation, lineage and mapping - Familiarity with cloud-based data architectures and API-driven flows - Experience with Microsoft Azure data platforms (desirable) Essential Skills - Excellent analytical and problem-solving abilities - Strong stakeholder management and communication skills - Ability to challenge constructively and influence decisions - Comfortable working with ambiguity and evolving requirements - Strong facilitation and workshop leadership capabilities - Ability to manage multiple priorities in fast-paced environments - High attention to detail with strategic perspective Personal Attributes - Naturally curious about how complex systems work - Challenges assumptions and seeks continuous improvement - Builds credibility quickly with business and technical stakeholders - Confident working with senior engineers and architects - Takes ownership and accountability for outcomes - Remains calm and pragmatic under pressure How to Apply To apply for this role please submit your CV using the form below or email info@blvdr.pl

• CI&T is looking for a Mid-level Data Analyst to work on a transformative project in the automotive industry, building the analytical layer for a mobile application at growing scale, connected to an ecosystem of vehicles that generate significant volumes of telematics data. • The professional will operate at the interface between product, engineering, and business, working with an AWS data infrastructure under development (S3, Athena, Redshift, QuickSight) and mobile analytics data (Firebase/Crashlytics). • The core challenge is twofold: understand app user behavior (engagement, retention, conversion, churn) and begin to explore the analytical potential of vehicle telematics data. • This is not about dashboards for the sake of dashboards — the mission is to help the team ask the right questions before any visualization, ensuring that analysis produces actionable recommendations and influences product prioritization.

• Analyze player behavior across the full funnel to translate findings into actionable product recommendations; • Design, run, interpret A/B tests and quasi-experiments; • Build and maintain cohort-based analyses to support product and investment decisions; • Define, validate, and monitor product KPIs and metric trees; • Partner with product managers to prioritize the roadmap; • Develop self-service dashboards and data sources; • Investigate anomalies in key metrics; • Maintain culture of rigorous, honest analytics; • Own web analytics implementation and data quality.