Data Analyst

Data Analyst (Mid-Level) Role Summary The Data Analyst is an individual contributor who has moved beyond supervised execution and is developing the independence, analytical depth, and stakeholder skills needed to progress to Senior level. They own smaller analytical domains end-to-end, contribute meaningfully to team standards, and begin to proactively surface insights rather than waiting to be directed. What You'll Do Analytics & Insight Delivery • Deliver analytical work end-to-end for assigned business areas — from scoping and analysis through to presentation and stakeholder follow-through — with minimal re-direction • Support ad-hoc analysis for internal stakeholders including Marketing, Product, Finance, and Account Management, providing interpretive framing alongside data outputs rather than data retrieval alone • Begin to proactively surface trends, anomalies, and data-driven observations to stakeholders, developing the instinct to flag what matters without being asked • Support A/B test experiments under the guidance of the Senior DA — including data preparation, metric tracking, and results summaries — building toward independent experimental design at Senior level Google Analytics & Data Collection • Support the Senior DA in GA4 reporting, tracking plan validation, and custom dimension and metric implementation • Develop working knowledge of GA-to-warehouse integration and tagging practices in preparation for owning this domain at Senior level BI Development & Semantic Layer • Design, build, and maintain Looker dashboards and Explores for assigned business areas, owning deliverables with limited rework • Develop LookML including dimensions, measures, derived tables, and Explore configurations — ensuring all work is documented and consistent with team standards • Contribute to the shared semantic layer by flagging metric definition inconsistencies and proposing resolutions in collaboration with the Senior DA and Analytics Engineering team • Expand self-service analytics for non-technical stakeholders, enabling them to answer their own questions and reducing ad-hoc request volume to the team • Maintain accuracy and consistency of production Explores and dashboards as underlying data warehouse structures evolve Data Quality & Platform Collaboration • Take ownership of data accuracy for assigned dashboards and analytical outputs — proactively investigating discrepancies and escalating upstream issues to Data Engineering with clear root-cause evidence • Write performant SQL including window functions and CTEs; collaborate with Data Engineers on query optimisation and derived table design • Develop a working understanding of data pipelines feeding owned dashboards sufficient to diagnose freshness and quality issues and communicate resolution timelines to stakeholders • Begin collaborating with Analytics Engineers on LookML architecture and semantic layer requirements for owned areas Standards & Collaboration • Follow and actively contribute to coding standards, SQL style guides, LookML conventions, documentation requirements, and PR review processes • Participate in code reviews, providing constructive and consistent feedback that builds quality across the team • Support Junior Analysts through paired working and direct feedback, under the guidance of the Senior DA • Partner closely with Senior Analysts, Data Engineers, and business stakeholders; ask clarifying questions early and communicate proactively on blockers and timelines AI-Augmented Analytics • Apply AI and LLM tools hands-on to accelerate day-to-day analytics workflows — including SQL generation, LookML authoring, documentation, and exploratory analysis • Begin experimenting with AI-assisted insight narration and anomaly detection, sharing learnings with the team • Develop familiarity with self-serve analytics agent concepts (Cortex Analyst or equivalent) in preparation for owning this capability at Senior level Continuous Learning • Actively develop skills across advanced SQL, Python, statistical analysis, and cloud data platforms • Build toward independent ownership of experimentation, GA4, and stakeholder advisory at Senior level About You Required • Bachelor's degree in a quantitative field such as Mathematics, Statistics, Computer Science, or Engineering — or equivalent practical experience • 2–4 years of experience in a data analytics or BI role • Strong SQL skills including window functions and CTEs • 2+ years of hands-on experience with Looker and LookML development • Experience with AWS or Google Cloud data warehouses (Redshift or BigQuery) • Demonstrable experience building dashboards and Explores for non-technical business users • Working knowledge of software development best practices: Git, PR reviews, code style guides • Working knowledge of agile development processes • Ability to translate business questions into analytical tasks with growing independence • Clear, confident written and verbal communication with business stakeholders Preferred • Exposure to A/B testing concepts and basic experimental design • Foundational Python skills for data manipulation or automation • Familiarity with GA4 reporting and tracking fundamentals • Experience with AI or LLM tools applied to analytics workflows • Experience with Linux/OSX command line and Git • Understanding of ELT pipeline concepts and cloud data infrastructure • Prior experience mentoring or supporting more junior colleagues Thriving at Tillster As a member of Tillster, you will embody our core values: · Put Customers First: Prioritize the needs and satisfaction of our customers in all decisions and actions appropriate to Tillster’s stage of development, resources, and stated goals. · Collaborate: Work together effectively, leveraging diverse perspectives to achieve common goals. · Innovate: Embrace creativity and pursue new ideas to drive progress and improvement. · Operate from Data: Use strong critica