Data Analyst – Data & Reporting Engineer

Job Title

Data Analyst – Data & Reporting Engineer

Location

Austin, TX

Key Responsibilities

Workflow Automation & AI Agents

Identify the team's recurring reporting and analysis workflows and rank them by business impact and time saved. Design, build and maintain AI agents that run these workflows end to end, from data extraction through validation to the finished output.

Skill Development

Build and maintain reusable skills that support the agents. Each skill packages the business rules, data definitions, query logic and output standards so that every automated run produces consistent, trusted results.

B&E Data Expertise

Develop and maintain deep knowledge of B&E data structures, measures and business rules, including actuals, pipeline, units and revenue, channel and partner views, customer hierarchies and fiscal-calendar logic. Act as the go-to expert for how the data should be used and interpreted.

Data Analysis & Insight Generation

Perform in-depth analysis of B&E 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 and automation use cases across functional domains, ensuring alignment with business priorities and the future-state data strategy.

Business-to-Technical Translation

Engage with stakeholders to translate business requirements into clear technical specifications, analytical models, agent and skill designs, and actionable deliverables.

Impact Assessment

Analyze current-state data, processes and infrastructure against future-state goals. Identify data and process gaps, define mitigation strategies, and measure the business outcomes delivered by automation.

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.

Testing, Validation & Reconciliation

Validate new, transformed and AI-generated outputs against systems of record to ensure integrity and alignment with business requirements. Identify and explain variances before outputs reach stakeholders.

Reporting & Visualization

Build and maintain insightful dashboards, reports and executive-ready visuals, using AI-assisted tools to accelerate delivery.

Governance & Knowledge Management

Collaborate with BPR to support the business glossary, data dictionary, metric definitions and metadata for assigned domains, aligned with governance standards, business leadership and global teams. Document agents and skills so non-technical team members can run and extend them.

Cross-functional Collaboration

Partner closely with data engineers, product managers and business SMEs to align solutions with business objectives.

Required Skills & Qualifications

6–10 years of experience in data analytics, business analysis, data consulting or data management, with a proven ability to influence business decisions.

Advanced SQL expertise, with the ability to query and optimize large, complex datasets.

Proficiency in Python for data analysis, transformation and automation (Pandas, NumPy).

Hands-on experience building with AI/LLM tools

agents, prompt and skill design, tool calling, or agentic coding assistants (e.g., Claude Code).

Experience building dashboards with AI-assisted or similar visualization frameworks for data storytelling.

Deep understanding of data architecture and governance.

Proven track record of identifying and scoping new data use cases and converting insights into business recommendations.

Strong analytical thinking, stakeholder management and communication skills, including comfort presenting to senior business leaders.

Experience with Tableau, Snowflake and AI/LLM environments.

Familiarity with D&B data hierarchy, enrichment or similar reference data systems.

Experience with sales, channel or pipeline data in a B2B or education context is a plus.

Environment & Tooling Stack

SQL (Snowflake, PostgreSQL, Redshift)

Python (Pandas, NumPy, automation scripting)

AI agent and skill frameworks (e.g., Claude Code, MCP integrations)

AI-assisted visualization and Tableau

GitHub for version control