Sr. Data Analyst

Overview

On Site
Depends on Experience
Contract - W2
Contract - 12 Month(s)
No Travel Required

Skills

Data Analysis
Data Architecture
Data Dictionary
Data Integration
Data Management
Data Profiling
Databricks
Python
NumPy
Pandas
SQL
PostgreSQL
Apache Spark
Apache Hadoop
Bitbucket
Amazon Redshift
Git
JIRA
Confluence
Business Intelligence
Business Analysis
Dashboard
Documentation
Reporting
Tableau
Version Control
Snow Flake Schema
Meta-data Management

Job Details

Job Title: Sr. Data Analyst

Location: Cupertino, CA / Austin, TX (Onsite)

Duration: 6-12 Months

Detailed Job Description:

  • 8 12 years of experience in data analytics, business analysis, or data management.
  • Advanced SQL expertise with ability to query and optimize large, complex datasets.
  • Proficiency in Python for data analysis, transformation, and feature engineering (Pandas, NumPy).
  • Experience with Tableau or similar BI visualization tools for storytelling and dashboards.
  • Deep understanding of data architecture, governance, MDM, and data lineage.
  • Proven track record in identifying and scoping new data use cases and converting insights into business recommendations.
  • Strong analytical thinking, stakeholder management, and communication skills.
  • Experience with Snowflake, Databricks, or Hadoop/Spark environments.
  • Exposure to data catalog or governance tools (Collibra, Alation).
  • Familiarity with D&B data hierarchy, enrichment, or similar reference data systems.
  • Environment & Tooling Stack
  • SQL (PostgreSQL, Snowflake, Redshift)
  • Python (Pandas, NumPy, feature engineering) Tableau for visualization Git / Bitbucket for version control JIRA / Confluence for project tracking and documentation

Roles and Responsibilities:

  • Data Analysis & Insight Generation: Perform in-depth analysis of enterprise data to uncover trends, patterns, and correlations. Identify actionable insights and data-driven opportunities that inform decision-making and strategic initiatives.
  • Data Use Case Identification: Collaborate with stakeholders to identify and define data use cases across functional domains, ensuring alignment with business priorities and future-state data strategy.
  • Business-to-Technical Translation: Engage with stakeholders to translate business requirements into clear technical specifications, analytical models, and actionable deliverables.
  • Impact Assessment: Analyze current-state data and infrastructure versus future-state goals; identify data and process gaps; define mitigation strategies.
  • Data Profiling & Quality Analysis: Perform deep dives into structured and semi-structured datasets to assess completeness, consistency, and accuracy; work with SMEs to understand data availability and source gaps.
  • Data Integration & Transformation: Design and validate data pipelines and transformations; document lineage and dependencies.
  • Feature Engineering & Analytics: Use Python and SQL to extract, clean, and engineer data features for analytics and modeling.
  • Reporting & Visualization: Build and maintain insightful Tableau dashboards and reports to communicate findings effectively.
  • Governance & Documentation: Maintain business glossary, data dictionary, and metadata aligned with governance standards.
  • Testing & Validation: Participate in validation of new or transformed data sets to ensure integrity and alignment with business requirements.
  • Knowledge Management: Maintain the data dictionary and business glossary for assigned domains, capturing and documenting definitions aligned with business leadership and global teams.
  • Cross-functional Collaboration: Partner closely with data engineers, product managers, and business SMEs to align solutions with business objectives.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.