Data Analytics Consultant

Aasritha Infotech Inc

Washington, United States

3 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Experienced

Experience required

3 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Amazon Web Services Business Analytics Applications Data Analysis Microsoft Azure Big Data BigQuery Information Systems Computer Programming Information Engineering Extract Transform Load (ETL) Data Visualization Data Warehousing

+16 more

Python (Programming Language) NumPy Performance Tuning Power BI SQL Databases Tableau (Software) Google Cloud Cloud Platform System Snowflake Pandas Scikit Learn Information Technology Data Analytics Tools for Reporting Looker Analytics Databricks

Requirements

The Data Analytics Consultant to help clients harness data to improve business performance and drive strategic outcomes. In this role, you will analyze large datasets, build dashboards, and translate insights into clear, actionable recommendations. You will collaborate with cross-functional teams to define requirements, develop data models, and deliver analytics solutions that support decision-making. The ideal candidate brings strong problem-solving abilities, excellent communication skills, and experience with modern analytics tools. This position offers opportunities to innovate, influence client strategy, and support continuous improvement across analytics practices.

· Bachelor’s or Master’s in Data Analytics, Statistics, Computer Science, Information Systems, Economics, or related field.

· 3-7 years of experience in analytics, BI, or consulting (adjust years based on level).

· Demonstrated success leading analytics projects from scoping to delivery and adoption.

· Strong command of data modeling, SQL, and visualization best practices.

· Experience advising stakeholders and influencing decisions with evidence-based insights.

· Data Visualization: Power BI, Tableau, Looker (design systems, DAX/LOD, performance tuning).

· Data Engineering (light): SQL (joins, window functions, CTEs), ETL/ELT workflows, data warehousing concepts.

· Analytics: Statistical analysis (regression, time-series, clustering), experiment design, forecasting, scenario modeling.

· Programming (preferred): Python (pandas, NumPy, scikit-learn), or R (tidyverse).

· Cloud & Data Platforms (preferred): Azure (Synapse, Fabric), AWS (Redshift, Athena), Google Cloud Platform (BigQuery), Snowflake, Databricks.

Education Level: Bachelor’s or Master’s in Data Analytics, Statistics, Computer Science, Information Systems, Economics, or related field