117 applicants

Company

PSS Technology Inc · 1 day ago

Sr Data Analyst- Financial Transactions & Payment technologies

United States

Contract

Remote

Mid, Senior Level

4+ years exp

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Hiring Manager

Shubhanshu Gupta

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Responsibilities

Lead Analytics Projects: Independently gather requirements, identify relevant data, and write sophisticated SQL scripts to build data sets that enable you to generate insights through visualizations and presentations.

Provide Analytics Support Across Business Functions: Deliver actionable insights for lifecycle marketing, business development, operations, product, and finance to drive strategic initiatives.

Analyze Customer Transaction Behavior: Support analytics on customer transaction behavior, particularly for new product features and user adoption, to identify patterns and trends that align with business goals.

Create Visualizations and Dashboards: Design impactful visualizations and automated dashboards to reveal key trends and measure the effectiveness of various initiatives, from marketing to product launches.

Investigate Data Anomalies: Proactively investigate data anomalies and idiosyncrasies, uncovering underlying issues and opportunities that havent been explicitly requested.

Exceed Partner Needs: Leverage your knowledge of the business to anticipate and exceed partner needs, using initial requirements as a foundation and building upon them with novel insights.

Collaborate Across Teams: Work with internal and external stakeholders to define and deliver data, reports, visualizations, and dashboards critical to decision-making.

Ensure Data Quality: Maintain high data quality through rigorous documentation and quality assurance processes.

Drive Projects with Minimal Guidance: Take ownership and accountability for meeting contribution goals, consistently pushing projects forward independently.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Financial Transaction Data AnalysisData Analytics ExperienceAdvanced SQL ProficiencyData Visualization ToolsStatistical MethodsRPythonCross-Functional AnalyticsMachine LearningRelationship-Building Skills

Required

Bachelors Degree in engineering, economics, physics, or a related quantitative discipline.

4+ years of data analytics experience, ideally within a financial services or business analytics context.

Advanced SQL proficiency, including expertise with window functions, common table expressions (CTEs), performance tuning, and query optimization for large data sets.

Proven experience working with cross-functional data in marketing, product, finance, or operations.

Experience with data visualization and dashboard creation using tools such as MicroStrategy, Tableau, Power BI, Looker, QuickSight, or Excel.

Familiarity with statistical methods and experience using R or Python for in-depth data analysis.

Proactive Mindset: A natural problem-solver who identifies and investigates data inconsistencies or patterns, uncovering insights that drive business impact.

Strong communication skills, with the ability to explain complex analyses to both technical and non-technical stakeholders.

Excellent organizational and problem-solving skills, capable of transitioning from detailed analysis to high-level strategic insights.

Ability to influence and drive alignment through constructive dialogue without formal authority.

Flexibility in prioritizing tasks, selecting appropriate tools, and managing multiple projects with competing priorities.

Strong relationship-building skills and a desire to collaborate in a high-energy, team-oriented environment.

Preferred

LinkedIn with a recommendation will be preferred first

Experience in analyzing and mining financial transaction data for insights, with an understanding of payment technologies, payment types, credit/debit card networks, and purchase authorization/settlement processes.

Experience in building and deploying predictive models (regression, classification, clustering, time series forecasting) using Python, R, or similar tools.

Familiarity with machine learning algorithms and their application in deriving insights from large datasets.

Experience working on projects across multiple business areas such as marketing, product, finance, and operations translating data into actionable business insights.