Digital Analytics Lead

Allied Solutions•Carmel, IN

About The Position

The Digital Analytics Lead owns and advances the organization’s Digital Analytics capability over the long term. This role defines the standards, frameworks, and governance that ensure user behavior is measured and reported consistently, accurately, and at scale across products and platforms. This role serves as the organization’s subject matter expert in digital and behavioral analytics and is responsible for establishing and maturing this capability in close partnership with Data Engineering, Data Governance, and Business Intelligence teams. Serving as a lead-level individual contributor, the Digital Analytics Lead partners closely with UX, Product, Engineering, and Strategic Leadership to translate behavioral data into actionable insight aligned to inform software product decisions and achieve business outcomes.

Requirements

Bachelor’s degree in Analytics, Data Science, Human Computer Interaction, Psychology, Information Systems, Business, or a related field, or an equivalent combination of education and practical experience.
5+ years relevant experience in behavioral, digital, or product analytics within complex digital or software environments.
Significant experience in behavioral or web analytics supporting decision making across products or platforms.
Demonstrated experience owning or leading analytics strategy, measurement frameworks, or implementation standards across multiple products or teams.
Handson experience with event based analytics and instrumentation, including defining event models, taxonomies, and data quality practices.
Working experience using query languages (e.g., SQL or similar) to explore, validate, and analyze behavioral data across analytics platforms and/or enterprise data stores.
Proven ability to analyze behavioral data and translate findings into clear, actionable insights that inform product, UX, and business decisions.
Strong understanding of data modeling concepts related to digital analytics (e.g., event schemas, sessionization, dimensional thinking).
Experience working with or alongside cloud data warehouse environments (e.g., Snowflake) and understanding how behavioral data is structured for analysis.
Experience collaborating with Data Engineering and/or Business Intelligence teams to translate behavioral tracking into usable datasets and reporting.
Understanding of data privacy, consent management, and responsible handling of behavioral data in regulated or enterprise environments.
Strong communication and collaboration skills, with the ability to explain analytical and technical concepts to cross functional partners.
Experience partnering closely with UX, Product, Engineering, and Business stakeholders to align measurement with outcomes.

Nice To Haves

Master’s degree preferred.
Experience using automation and AI assisted analysis techniques to support exploratory analysis, pattern detection, anomaly identification, or insight generation, with sound judgment around appropriate and responsible use.
Experience working within UX, product, or digital experience organizations.
Experience with modern behavioral and web analytics platforms (e.g., Google Analytics 4, Google Tag Manager, Microsoft Clarity, or similar event based tools).
Experience designing, building, and stewarding dashboards or reporting frameworks, including metric definitions, documentation, and ongoing maintenance.
Familiarity with experimentation, optimization, or iterative measurement approaches.
Experience designing or supporting experimentation frameworks (e.g., A/B testing, feature validation, behavioral impact measurement).
Experience enabling, coaching, or mentoring others in analytics practices without formal people management responsibility.

Responsibilities

Define and Govern Data Measurement Strategy – 25% Define and evolve behavioral measurement frameworks aligned to product, UX, and business goals. Establish priorities and maturity roadmaps for behavioral and digital analytics across platforms and initiatives. Define and govern behavioral metrics that reflect software utility, usability, user success, and outcomes. Consult with cross-functional teams to recommend appropriate analytics tools, metrics, and reporting approaches, advising on measurement strategies based on efficiency, implementation effort, data availability, and insight value. Evaluate, pilot, and apply analytics tools and platforms to support measurement goals, usability, data quality, and decision impact. Make hands-on recommendations informed by direct use of analytics tools and data, not abstract evaluation alone. Serve as a trusted advisor to UX, Product, Engineering, and Business leadership on behavioral metrics interpretation and appropriate tool usage. Act as the central steward for disciplined, consistent, and decision focused measurement practices.
Digital Analytics Implementation and Data Quality – 25% Directly design, implement, and maintain event taxonomies, tagging strategies, naming conventions, and implementation standards. Partner hands-on with Engineering to implement analytics instrumentation when application code changes are required. Personally configure, test, validate, and audit analytics platforms (e.g., GA4, GTM, behavioral analytics tools). Identify and remediate tracking gaps, data inconsistencies, and implementation issues. Establish and run processes for ongoing behavioral data quality monitoring and validation. Ensure analytics implementations remain scalable, reliable, and production ready over time.
Enterprise Data Integration & Collaboration – 20% Work hands-on with Data Engineering, Data Governance, and Business Intelligence teams to ensure behavioral data is structured, integrated, and usable across enterprise systems. Define and validate behavioral data requirements for ingestion into enterprise data platforms (e.g., data warehouses and BI tools). Contribute directly to event schemas, data models, and patterns that connect behavioral data across platforms, sessions, and journeys. Ensure behavioral analytics implementations align with enterprise data architecture and downstream analytics needs. Serve as an active cross-functional bridge between UX, Product, Engineering, and Data teams.
Data Analysis, Insight, & Reporting – 15% Hands‑on analysis of behavioral data to generate actionable insights that inform product, UX, and business decisions. Design, build, and maintain dashboards and performance views that surface meaningful behavioral patterns. Use query based analysis, automation, and AI assisted techniques to explore data, validate metrics, and accelerate insight generation. Execute behavioral segmentation based on intent, journey stage, and interaction patterns. Synthesize and connect insights across products and user journeys to support enterprise level decision making and prioritization.
Documentation, Mentorship & Analytics Enablement – 15% Establish and maintain analytics standards, governance frameworks, and implementation guardrails. Document metric definitions, instrumentation patterns, dashboard logic, and reporting conventions to ensure clarity and consistency. Develop playbooks, templates, and educational materials informed by real implementation and analysis experience. Provide hands-on peer mentorship, coaching, and collaborative working sessions with UX, Product, Engineering, and Analytics partners. Partner directly with teams to ensure reports, dashboards, and metrics are understandable, usable, and decision ready — not just technically correct. Facilitate shared interpretation and sense‑making of analytics outputs through working sessions, reviews, and applied guidance. Enable teams to responsibly engage with analytics while maintaining centralized stewardship, data quality, and analytical rigor. Support analytics maturity through education, feedback, and shared learning — without formal people management responsibility. Balance immediate business needs with long-term data integrity, scalability, and sustainability.