Everway
Director, Analytics & Decision Intelligence
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Director, Analytics & Decision Intelligence
Senior Analytics & Decision Intelligence Product Lead
At Everway, we're here to break down the barriers that keep too many people feeling invisible in the world of work. We design technology that helps everyone understand—and be understood. Every mind is unique, yet much of the world is still built for what’s considered "normal." Our careers fit real life. When you join Everway, you’re not just taking a job—you’re joining a mission to establish a neuroinclusive world.
We’re a global organisation of over 800 employees across Australia, New Zealand, the UK, Europe, North America, and beyond. We deliver tangible, cutting-edge solutions in product, design, marketing, sales, support, finance, operations, and data. As an organisation, Everway stands out because our culture and processes are built for humans—not just code.
Our people are encouraged to be curious, courageous, and fully committed. We strive to support the richness of the minds around us, fostering a workplace where different ways of thinking are celebrated and encouraged.
About the Role 🚀
Every area at Everway—from sales and finance to product and customer success—functions under rapid, dynamic decision-making. However, too many of these decisions are often made based on intuition rather than the confidence and precision of high-quality data insights.
This sole role is being established to develop and lead analytics as an intentional, decision-first function. You’ll partner with our partners, stakeholders, and teams across sales, product, marketing, and customer success to ensure that every important decision is being informed in a thoughtful, we’re-hearted way.
In this role, you will:
- Assume leadership of the analytics function, translating business pain points to actionable insights through carefully-scouted analytics products.
- Lead daily decisions to deliver on outcomes with a data-first mindset and drive adoption across teams—from commercial to product to leadership.
- Develop a vision for a new tech stack, including AI-powered analytics, and determine its ability and functionality in augmentation of higher-quality decision-making.
- Partnership with our SVP of Engineering and Data, as well as internal data teams, to define the right processes and infrastructure, ensuring the team has the wholesome resources required.
Key responsibilities include – but are not limited to –
1. Identify and transform critical business decisions
- Work across departments to map and prioritize decision points that require better data to optimise outcomes.
- Collaborate with commercial, product, marketing, and leadership teams to ensure insights directly drive revenue, retention strategies, and lifelong learner outcomes.
2. Transform, nourish, and enhance exploration
- Move your teams forward by shaping structures for a robust self-service approach: certified datasets, governed access frameworks, and intuitive discovery surfaces.
- Uncover data blind spots through exploratory analytics, anomaly detection, and emerging trends, subsequently preemptively driving better decisions.
3. Product-focussed analytics team leader
- Set product vision aligned to business needs, overseeing the backlog and operating model—organised around confident decision support rather than individual tracking.
- Foster a culture of ownership and quality assurance, which ensures everything delivered enhances real-world effectiveness of business decisions.
4. Semantic layer ownership
- Define and defend metric logic, Enterprise Product Integrations, and data source credibility, making sure definitions iterate logically and transparently.
5. Collaborate with the data engineering teams
- Partner to design and optimise data contract-casually solid Yamaleon architectures in Databricks—with insights reflecting operational efficiency and data governance.
6. BI platform management & growth evaluation
- Dedicate full ownership to our existing platform, currently Tableau, while assessing Bi platform migration as opportunities emerge.
- Establish and enforce governance standards, from certification procedures to data source performance for scale.
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7. Govern AI-powered analytics advancement
- Lead the guided exploration and transformation of AI capabilities within analytics—including automated insight delivery via natural language interfaces and LLM-powered workflows.
- Develop a balanced approach, choosing when to harness AI for insight, and when to retain governed product presentations for rigorous integrity and transparency.
8. Drive data literacy and decision-readiness
- Actively build a culture of data-centric advocacy, uplifting skills and confidence through engagement sessions, curated learnings, and showcasing change in decision quality empowers teams.
9. Ensure safe, data trustworthiness through discovery efficiency
- Maintains clear организационные storages 文化 catalogue records such as data lineage, freshness, and ownership data visibility.
10. Scale up analytics engineering standards
- Elevate the baseline by rigorously applying standards for dashboard design patterns, naming conventions, and version control.
- Documentation reinforcement, testing protocols, and peer reviews ensure standards lift at every process level.
11. Measure and continuously enhance product impact
- Evaluate feedback and outcomes through traditional database metrics and insights on analytics functionality—ensuring data-enhanced decisions positively shape the business roadmap.
12. Lead a talented team that scales with the evolving needs
- Mentor analysts toward heightened technical and product thinking, prescribing how superior data can address clever human challenges and drive unbounded productivity.
Responsibilities
Core Accountabilities:
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Mapping business decisions and prioritising high-value improvements: Work cross-functionally with stakeholders (product, sales, finance, customer success) to identify and prioritise analytic dependencies—data we’re missing, inconsistencies we endure, and trends that change accelerated progress.
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Scooping forward-looking business expansion points: Arguably the best-shifting stance in ubiquitous analytics occurs when we’re triggering reactions—not connecting them—through foundational discovery.
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Guiding the analytics team’s formation and productivity growth: Functioning as a first-choice product leader, you will align stakeholders with team objectives, establish ownership-driven rigorous processes, and nurture top-tier talent.
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Embedding decision-driven design choices inside business outcomes: Flip data into a collaborative partner, rather than a confusing add-on, where every Analytics team member underscores the ** outcomes for individual decisions represent real progress.
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Leveraging Databricks as a scalable data fabric for the team’s products: Translate business requirements into finite data constructs, integrating collaboration between ETL and analytics teams to prepare asset quality at the original source itself.
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Standardising and supporting BI on the Tableau platform: Lead | Develop meticulous execution of certified content, publishing governance, and extensive instrumentation of SLAs & access needs.
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Aligning and executing inclusivity of AI: Set specific governance policies for AI workshop styles and sustained governance in analytics supplemented by LLMs to optimize both productivity and precision.
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Fostering Data Literacy and self-sufficiency across teams: Partner pursuing tailored data insights, presentation, and framework empowerment to improve individual choices and collective decision-making.
Adjacent Responsibilities:
- Set up and maintain a catalogue of metadata systems for certified solutions, ensuring visibility, documented provenance, and accessibility.
- Maintain testing, benchmarking, and reassessment programs ensuring metrics track to evolving ROI thresholds.
- Balance scalar tooling and strategy for analytics without compromising democratization.
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Requirements
Essential
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3+ years of applied experience in business intelligence, analytics (~6-12 months minimum senior management or leadership roles required)
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Advanced proficiency must reside in some or all of the following domains:
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Tracking difficult questions with nuanced logic functions including:
- SQL (comprehensive 8+ years interaction)
- How the managed lakehouse (Databricks, Snowflake, or similar) with relation optimization, performance metrics.
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Business intelligence platforms (curr: Tableau) and standard governance: Tableau knowledge validated across certified workspaces, asset governance, analytics transformation.
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Developing data products (integrating ownership and documentation) with customer-focused approach that evolves from raw models to calibrated documentation.
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Crafted self-service analytics layers—thanks to accessible API spaces and tiered dairy practices, along with guiding metamodelling-driven analysis.
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Unrivalled reputation in Semantic Design, metrics architecture ideas, and its governance strategies that culminate cohesive decision frameworks.
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Classified hands-on leadership within a high-junction self-service analytics or data stack, delivering results: quantifiable, iterative, with ارية experimentation reinforcement.
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Understanding challenging supply chain-focused webhooks: Designing dev-ops materials that incorporate product spinularity.
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Clear communication skill, guaranteed expertise in stakeholder engagement tactics, from engaging hesitant data leaders to complying over disagreements with preferred outcomes.
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**As we grow, so must you—**proven history of team leadership, demonstrated through hiring/developing high-performing teams.
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Pioneering AI-integrated BI: Leverage AI as a syntactic example in Tableau, potentially conseguir and reintroduce automated insight intelligence (genetic NLP, predictive scaling).
Desirable
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Deep practical Databricks experience[Delta or Unity Catalog].
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Proactive **dbt (with MetricFlow incorporation or data contracts instantiation.push/governance).
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AI powered analytics is a top-flow priority. “Dedication to hands-on integral evaluations rest” is mirrored within Pulse, Genie perspectives & Tableau LLM-supported analyses.
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Mastery in data quality, Great Expectations, or Monte Carlo implementations.
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Plant intellectual roots in genetic inference or statistical transformation regarding Databricks Analytics transformations.
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Motivate the SaaS UX outcome arena, exhibiting knowledge of metrics (NARs, marketing efficiency, revenue teams) versus core user-behaviour.
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Jupyter notebook navigation, surety of data pipeline maintenance trains.
Benefits
- above-average core pay remuneration with outstanding yearly bonus potential.
- Deep infra-tech advantage: +Time freedom robotically jointly adopt hybrid or fully virtual.
- Building mini-planets for growth treatment: thorough encouragement, devoted development programs, tailored mentorship.
- Wendy and wellness liberation: embrace our CalmCare well-being infrastructure.
You are invited to fulfill this development pathway, in this impactful Conference energy Women Grand Finale.
Equality and Diversification Principles:
Everway commits to cultivating equally supportive work environments—valuing humane shields and meritocracy is seasoned for general faith in diversity and fearlessness. Our **"never say no / unstoppable الماضي" vision *combines dignity, inclusion.
Fair Enterprise Fairstand!
Resultant applications will be pursued BEFORE 3 July, 2026, and given special competitive interest considering their stellar candidates.
Please apply with:
📤 LinkedIn 📄 Applications Receiving Automation待この時刻义务!
Apply today on our recruitment webpage: [Everway Careers](insert link)
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