Data Analyst - Tableau/Power BI

Position Overview :

We are looking for a skilled and self-driven Data Analyst with 35 years of experience who can independently handle end-to-end analytics from querying raw data sources and building data models to delivering polished, visually compelling dashboards. The ideal candidate combines strong Tableau development expertise with data engineering exposure, including experience working with streaming/real-time data, semantic layers, and cloud-based data platforms (GCP/BigQuery).

Key Responsibilities :Data Modeling & Architecture :

• Design and build dimensional data models (star schema, snowflake schema) optimized for analytical workloads.

• Define fact and dimension tables, establish relationships, and manage slowly changing dimensions.

• Build and maintain a semantic layer to provide consistent, business-friendly definitions of metrics and KPIs.

• Create reusable, well-documented data models that serve as a single source of truth.

Data Querying & Engineering :

• Write advanced SQL queries for data extraction, transformation, and validation across large-scale datasets.

• Query data directly from source systems (databases, APIs, cloud storage, streaming platforms).

• Work with streaming/real-time data pipelines understand event-driven architectures, time-windowed aggregations, and near-real-time refresh strategies.

• Build and optimize Tableau Extracts and live connections for performance at scale.

• Collaborate with data engineers on ETL/ELT pipelines, data quality checks, and pipeline monitoring.

• Handle data at granular levels (minute-level, event-level) and aggregate appropriately for reporting.

Visualization & Dashboard Development:

• Design and develop enterprise-grade Tableau dashboards that are visually appealing, intuitive, and standards-compliant.

• Select the right chart type for the right data story:

• Time-series line charts for trends

• Heat maps for comparative/density analysis

• Bar/column charts for ranking and composition

• KPI cards and scorecards for executive summaries

• Scatter plots for correlation

• Geographic maps for spatial analysis

• Bullet charts and sparklines for compact performance views

• Follow data visualization best practices (Tufte principles, proper use of color, minimal chart junk, accessible design).

• Build interactive elements: cascading filters, parameters, drill-downs, tooltips, and dynamic actions.