Data Analyst (Part-Time Contractor)

Stable Kernel•Atlanta, GA

1d•Hybrid

About The Position

As a Data Analyst at Stable Kernel, you will play a key role in converting data into insights that inform client and internal product roadmaps. Working with cross-functional teams, you will independently design dashboards, build reporting queries, and translate high-level questions into measurable metrics. Your blend of analytical, communication, and problem-solving skills will help clients evaluate how their products operate and identify opportunities for improvement. Data Analysts report directly to our Principal Data Strategist.

Requirements

3-5 years of experience as a data analyst.
Experience working in a consulting or professional services organization.
A bachelor’s degree in data science, a quantitative/analytical field, or similar.
Familiarity with data warehouses, product analysis, statistical analysis tools and experience with large data sets and has managed their reliability and consistency.
Experience with CRM tools such as MoEngage.
Experience with data activation tools such as Hightouch.
Experience using SQL and Python.
Experience with observability and data visualization tools such as Grafana ,DataDog, Tableau, Looker or PowerBI.
Experience using analytics related tools such as Databricks or Amplitude.
Familiarity with Microsoft Office Suite.

Nice To Haves

Ability to create ER diagrams.
Ability to effectively test APIs using standard tools such as Postman and curl.
Experience using agile work management tools such as Jira.

Responsibilities

Designing and building dashboards and reports that align with business objectives, using advanced features of business intelligence tools (e.g., complex SQL, calculated fields and advanced charting).
Defining success metrics and measurement plans for new products and features.
Tracking design and data requirements, suggesting scalable and reusable solutions.
Monitoring dashboard and data health, investigating anomalies and resolving data quality issues.
Contributing to documentation updates and maintaining data dictionaries to ensure shared understanding across projects.
Improving the efficiency or usability of existing reports and solutions by creating reusable queries or components and training junior analysts on tool basics.
Collaborating with product and engineering to understand technical dependencies and translate business questions into data solutions, sharing insights during cross-functional meetings.
Translating high-level client or project goals into data questions and metrics; validate that analyses align with these objectives and advise on measurement priorities.
Providing actionable insights that inform decisions and connect analysis outcomes clearly to business KPIs, suggesting optimizations based on trends or findings.
Breaking down analysis tasks, provide accurate estimates, and communicate expected effort and trade-offs while factoring in data complexities.
Building a strong understanding of the client domain and user journeys, incorporating domain nuances into analyses and understanding dependencies with engineering, product and marketing processes.
Supporting development of reusable dashboards, frameworks and templates, contribute to internal best practices and documentation, and share learnings across projects.
Coordinating closely with cross-functional teams to understand dependencies and identify data needs or risks early.
Building trust through reliable delivery and begin advising on analysis prioritization based on business impact.
Supporting roadmap discussions with data-backed insights, help triage competing requests and propose small enhancements where data can add value.
Suggesting improvements to existing metrics or frameworks and contribute to proposal or workshop preparation.
Coordinating analytics tasks within cross-functional pods, clarifying dependencies with product, design and engineering teams.
Supporting project planning and sprint alignment by providing clear, structured updates in stand-ups and reviews.
Sharing insights that inform product or marketing decisions, recommending reporting improvements or new analyses and advising on best practices for metrics interpretation.
Supporting teammates by providing tool and process guidance, proactively communicating risks and mitigation plans, and documenting progress in shared tools.
Solving moderately complex analysis challenges independently by proposing hypotheses, validating with data and recommending actionable insights.
Defining scope when details are unclear, suggest initial approaches to move work forward and clarify assumptions in analysis.
Identifying data or analysis risks and propose mitigation steps, communicating risks clearly to project teams and escalating blockers or risks appropriately with context.
Documenting troubleshooting steps before escalation.
Identifying inefficiencies in workflows within the team and suggest and implement process improvements.
Promoting data quality and consistency by encouraging adherence to standard metrics and definitions and providing guidance to junior analysts on tools and approaches.
Sharing knowledge in informal trainings or code reviews and lead small analytics projects or pods, coordinating task execution and dependencies to ensure alignment on goals.