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Data Analytics Engineer (Finance)

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MexicoJobPosted 1y agoStill listed 4 days ago

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At a glance

Compensation
No compensation found
Location
Mexico
Work Authorization
Not specified

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Requirements

Credentials this posting asks for.

Bachelor's degree

Job overview

The Data Analytics Engineer will support finance, accounting and regulatory reporting by ensuring data accuracy and compliance. The role involves developing innovative data‑driven solutions, building business‑intelligence tools and dashboards, and managing data quality, security and governance across credit‑product operations within a fintech environment.

Skills & qualifications

RequiredNice to have

Skills

Data AnalysisData ManagementBusiness Intelligence DevelopmentData QualityFinancial RegulationsBasic Accounting PrinciplesData VisualizationCollaborationProblem SolvingInnovationSQLPythonInterpersonal SkillsDiplomatic SkillsInfluencingPlanningOrganizational SkillsCommunicationRegulatory ReportingCredit Product OperationsAccounting PrinciplesOpen Source TechnologiesMetadata ManagementData LineageSecurity MechanismsData Access Governance

Qualifications

Bachelor's Degree in Engineering, Science, Operations Research, Information Technology, Statistics, Mathematics, Economics, Finance, Analytics or Related Quantitative FieldTwo or More Years of Quantitative and Qualitative Analysis Using SQL or PythonTwo or More Years of Working Experience in Data AnalyticsEnglish Fluent/Proficient

Full job description

As a Data Analytics Engineer at Stori, you will leverage your analytical and technical expertise to support our finance, accounting and regulatory reporting functions. Your role will be critical in ensuring data accuracy, compliance, and the generation of reports and reconciliations processes. On any given day, you will be challenged on three types of work – Innovation, Business Intelligence, and Data Management • A bachelor's degree or foreign equivalent in Engineering, Science, Operations Research, Information Technology, Statistics, Mathematics, Economics, Finance, Analytics, or a related quantitative analytical field.

  • Advanced skills in SQL and Python for data manipulation and analysis.

  • Proven interpersonal, collaboration, diplomatic, influencing, planning, and organizational skills.

  • Consistently demonstrate clear and concise written and verbal communication.

  • Proven ability to effectively use complex analytical, interpretive, and problem-solving techniques.

  • Demonstrated ability to work under pressure and to meet tight deadlines with proactive, decisiveness, and flexibility.

  • English Fluent/proficient.

  • 2 or more years of experience in quantitative and qualitative analysis using SQL or Python.

  • 2 or more years of working experience in data analytics.

  • Deep understanding of credit product operations (credit cards, loans, asset financing, etc.).

  • Understanding of financial regulations and accounting principles, especially within the fintech industry.

  • Experience with regulatory and compliance reporting in a financial services context.

Innovation

  • Develop and enhance data-driven solutions for accounting processes, including the automation of reconciliations and finance reporting to improve accuracy and efficiency.

  • Use Open Source/Digital technologies to mine complex, voluminous, and different varieties of data sources and platforms.

  • Build well-managed data solutions, tools, and capabilities to enable self-service frameworks for data consumers.

  • Demonstrate the ability to explore and quickly grasp new technologies to progress varied initiatives.

Business Intelligence

  • Collaborate closely with teams to launch new products or features, ensuring they are aligned with finance objectives.

  • Partner with the business to provide consultancy and translate the business needs to design and develop tools, techniques, metrics, and dashboards for insights and data visualization.

  • Drive analysis that provides meaningful insights on business strategies.

Data Management

  • Drive an understanding and adherence to the principles of data quality management, including metadata, lineage, and business definitions.

  • Work with business teams to understand their needs and translate them into technical requirements.

  • Work collaboratively with appropriate Tech teams to manage security mechanisms and data access governance.

  • Build and execute tools to monitor and report on data quality.

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