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…