Senior Data Analytics & Data Management Consultant

Ztek Consulting, Inc.; Austin (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

Ztek Consulting, Inc. in Austin, TX is seeking an experienced data analytics professional to drive insights, establish data use cases, and translate business needs into technical deliverables.

You will collaborate with data engineers, PMs, and SMEs to build robust pipelines, governance, and AI-assisted analytics that inform strategic decisions. Responsibilities include profiling data quality, designing pipelines, feature engineering, and creating compelling dashboards that tell data-driven

Qualifications

• 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 building dashboards using AI or similar visualization frameworks for data 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.
• Basic working knowledge of AI/ML concepts and exposure to AI-assisted tools (e.g., Copilot-style coding assistants, LLM-based chat/analytics tools, or prompt engineering for data tasks) - no deep ML modeling expertise required.
• 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.

Responsibilities

• Perform in-depth analysis of enterprise data to uncover trends, patterns, and correlations.
• Identify and define data use cases across functional domains, ensuring alignment with business priorities and future-state data strategy.
• Translate business requirements into technical specifications, analytical models, and deliverables.
• Analyze current-state data and infrastructure versus future-state goals; identify data and process gaps; define mitigation strategies.
• Profile data and assess quality; collaborate with SMEs to understand data availability and source gaps.
• Design and validate data pipelines and transformations; document lineage and dependencies.
• Engineer data features for analytics and modeling using Python and SQL.
• Apply basic AI-assisted analytics to accelerate data analysis, documentation, and insight generation.
• Build and maintain insightful dashboards and reports to communicate findings.
• Maintain data glossary and metadata governance.
• Validate new or transformed datasets for integrity and business alignment.
• Collaborate with data engineers, product managers, and business SMEs to align solutions with business objectives.

Skills

SQL Expertise

Python Pandas

Data Dashboards

Data Governance

Big Data Platforms

Data Architecture

AI/ML Basics

Version Control

Project Tools

Stakeholder Management

Data Catalog Tools

Tools

Snowflake

Databricks

Hadoop/Spark

Collibra

Alation

Git/Bitbucket

JIRA/Confluence

Data Catalog

Job description

• 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.
• AI-Assisted Analytics: Apply basic AI/ML and LLM-based tools (e.g., prompt-based data exploration, AI-assisted feature engineering, or generative AI copilots) to accelerate data analysis, documentation, and insight generation.
• Reporting & Visualization: Build and maintain insightful 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.

Required Skills & Qualifications

• 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 building dashboards using AI or similar visualization frameworks for data 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.
• Basic working knowledge of AI/ML concepts and exposure to AI-assisted tools (e.g., Copilot-style coding assistants, LLM-based chat/analytics tools, or prompt engineering for data tasks) - no deep ML modeling expertise required.
• 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.
• Python (Pandas, NumPy, feature engineering)
• Git / Bitbucket for version control
• JIRA / Confluence for project tracking and documentation