Data Analytics Engineer BI Specialty

Dashboard & Visualization Engineering | AI-Accelerated Analytics Delivery

CLIENT

Visa

EXPERIENCE

5–8 years

BI STACK

Tableau Desktop & Server

DATA STACK

AWS, Databricks, Hive, Airflow

ROLE SUMMARY

We are seeking a proactive Data Analytics Engineer with a BI specialty to design, build, and evolve high-impact dashboards for Visa business stakeholders. This role is dashboard-first: you will turn raw data into clear, intuitive, decision-ready experiences, applying strong UX/UI judgment and knowing the best way to represent each metric (chart choice, hierarchy, interactivity, and context). You will use AI tools (e.g., Claude, Copilot) as a daily accelerator to prototype dashboards in hours rather than days and to make fast, secure changes to the underlying SQL and data pipelines. You will work across a legacy Hadoop/Hive environment and the target AWS/Databricks platform, partnering directly with business users from requirement to adoption.

KEY RESPONSIBILITIES

- Dashboard Design & Development: Build, enhance, and maintain production Tableau dashboards (and other BI tools as needed) that are rapid, intuitive, and aligned to business KPIs.
- Metric Visualization & UX: Choose the right visual for each metric (trend, comparison, distribution, variance, funnel, KPI card), apply sound layout, color, and accessibility principles, and design for the viewer’s decision, not the data.
- AI-Accelerated Prototyping: Use AI tools to generate wireframes, mock datasets, calculated fields, and working prototypes quickly; iterate with stakeholders in short feedback cycles before hardening for production.
- SQL & Pipeline Changes: Use AI-assisted workflows to write, tune, and review SQL and PySpark; efficiently modify upstream tables, views, and pipelines to support new metrics, with proper testing and validation.
- Semantic & Data Modeling: Design curated, reusable data models and certified data sources (star schemas, aggregates, extracts) that give users consistent, trusted metric definitions.
- Performance & Reliability: Diagnose and fix slow dashboards (extract optimization, query tuning, calculation and LOD refactoring,



data-source design) and ensure data freshness and accuracy.
- Stakeholder Partnership: Gather requirements, translate business questions into metrics, run demos and UAT, and drive dashboard adoption through training, documentation, and usage feedback.
- Platform & Migration Support: Support the migration of reporting workloads from Hadoop/Hive to AWS and Databricks (Delta Lake, Unity Catalog), maintaining continuity for downstream dashboards.
- Governance & Quality: Apply data-quality checks, version control, access controls, and dashboard standards (naming, style guides, templates) in line with Visa’s security and compliance requirements.

REQUIRED TECHNICAL SKILLS

- BI & Visualization: Expert-level Tableau (Desktop and Server): calculated fields, LOD expressions, parameters, actions, dashboard performance tuning, and publishing/permissions. Equivalent depth in Power BI or Looker is acceptable.
- Dashboard UX/UI & Data Storytelling: Demonstrated portfolio of dashboards showing strong layout, visual hierarchy, chart selection, and accessibility; able to justify design choices for each metric.
- SQL: Advanced SQL (window functions, CTEs, complex joins, query tuning) across Hive/Spark SQL and cloud warehouses.
- AI-Assisted Development: Proven, hands-on use of LLM tools (Anthropic Claude, GitHub Copilot, or similar) to prototype dashboards, generate and refactor SQL/Python, and accelerate analysis, with disciplined validation of AI output.
- Data Engineering Fundamentals: Python (PySpark) for data transformation; working knowledge of Apache Spark and distributed concepts (partitioning, shuffling, caching, broadcast joins).
- Cloud & Platform: Experience working with Hadoop/Hive environments and experience on AWS (S3, IAM, Glue) and Databricks (Delta Lake, SQL warehouses).




- Data Modeling: Dimensional modeling and building BI-ready datasets, aggregates, and extracts.
- Engineering Practices: Git/version control, testing and data validation, and workflow orchestration awareness (Airflow, Control-M).

PREFERRED / NICE-TO-HAVE SKILLS

- Experience with Databricks AI/BI (Dashboards, Genie), Unity Catalog, or Databricks SQL for dashboard serving.
- Building AI-assisted analytics features: natural-language querying, LLM-generated insights or summaries, or lightweight AI agents (LangChain, Mosaic AI).
- Front-end or design tooling familiarity (Figma, HTML/CSS, JavaScript, Tableau Extensions API or embedded analytics).
- Experience in payments, financial services, or fintech data (transactions, risk, settlement, authorization metrics).
- Tableau Server administration, usage analytics, and CI/CD for BI assets; Tableau or cloud certifications.

QUALIFICATIONS

- 5–8 years of professional experience in BI, analytics engineering, or data engineering in a production environment, with a significant portion focused on dashboard development.
- Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related quantitative field, or equivalent practical experience.
- Portfolio or examples of production dashboards (screenshots or walkthroughs) demonstrating design quality and business impact.

SOFT SKILLS

- Proactive & Ownership-Minded: Anticipates stakeholder needs, brings ideas and prototypes rather than waiting for requirements, and follows work through to adoption.
- Communication & Storytelling: Explains metrics and design choices clearly to technical and non-technical audiences.
- Design Judgment & Attention to Detail: Cares about clarity, consistency, and accuracy; challenges requests that would produce misleading visuals.
- Adaptable & Quick Learner: Quickly adopts new AI and BI tools and improves ways of working; comfortable iterating in rapid cycles.
- Collaborative: Works effectively with data engineers, analysts, product owners, and business partners in a global, cross-functional setting.

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