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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Analyst - CRM - **Company:** Publicis Digital Experience - **Location:** United States - **Experience:** Expert - **Salary:** $73,150.0 - **Contract:** Temporary contract - **Skills:** Microsoft Excel, Business Logic, Big Data, Data Governance, Data Structures, Data Visualization, Database Queries, Python (Programming Language), Marketing Information Systems, SQL Databases, Data Streaming, Tableau (Software), Data Inconsistencies, Databricks - **Published:** September 24, 2026 - **Apply:** https://www.thejobnetwork.com/job/senior-data-analyst-crm-509979396 ## About the Role * 2-4 years in marketing analytics, CRM analytics, or related field * Strong SQL skills; able to independently extract, join, and validate data * Experience analyzing campaign performance across channels * Proven ability to translate data into business insights * Experience working with large datasets and complex data structures * Strong QA mindset; detail-oriented without losing speed * Clear communication skills; able to simplify complex findings, * Experience in automotive, CRM, or lifecycle marketing * Exposure to test/control design and incrementality measurement * Experience with Databricks, Python, or R for deeper analysis * Familiarity with Tableau or similar visualization tools (used for communication, not just reporting) ## Description The Senior Data Analyst is responsible for turning CRM and marketing data into clear, defensible business decisions. This role executes analysis with precision while identifying what is working, what is not, and where programs should adjust. This role sits at the center of delivery; ensuring data is accurate, analysis is sound, and outputs are ready to inform decisions. You will support one of our client's CRM programs across aftersales and retail touchpoints, working across data, analytics, and account teams to ensure programs are measurable, accurate, and improving over time. This is a high-accountability execution role. You are expected to deliver accurate analysis, surface key insights, and ensure outputs are ready to inform decisions. Responsibilities Core Responsibilities Performance Measurement and Impact Validation: Ensure programs are measured correctly and results are credible * Analyze CRM performance across channels (direct mail, email, digital) * Apply structured measurement approaches (test vs control, pre/post, matched comparisons where applicable) * Identify drivers of performance; audience, offer, timing, channel * Translate findings into clear "what to do next" implications Analysis Execution: Own the data and logic behind every output * Write and own SQL queries to extract, join, and validate data across sources * Execute recurring and ad hoc analyses for CRM programs (aftersales and retail support) * Ensure all outputs are accurate, complete, and aligned to business logic * Reproduce results consistently; no one-off logic that cannot be traced Insight Generation: Go beyond dashboards; explain what matters * Build and maintain reporting outputs (Tableau, Excel, etc.), while highlighting key drivers, anomalies, and performance shifts * Provide clear summaries of "what happened" and "why it matters" Data Ownership and Quality: Numbers are trusted because you validate them * Own QA/QC across datasets, queries, and final outputs * Validate campaign counts, audience definitions, and performance metrics * Identify and escalate data inconsistencies early * Ensure alignment between analytics outputs and campaign execution Execution Support Across Teams: Operate within the system, not in isolation * Partner with campaign, account, and data teams to support program execution * Understand how data flows through systems and where issues can occur * Support data pulls, audience validation, and post-campaign analysis Structured Problem Solving: Turn ambiguity into clear analysis quickly * Break down ambiguous requests into clear analytical steps * Investigate discrepancies and performance issues with a methodical approach * Prioritize practical answers over perfect ones when timelines require it Communication and Deliverables: Make analysis usable * Contribute to decks, summaries, and readouts with clear, concise language * Present findings internally; support client-facing materials as needed * Focus on clarity and accuracy; avoid overcomplicating the message Qualifications Working Style and Characteristics * Owns the numbers end-to-end + Doesn't pass along outputs without validating them. If something looks off, they stop and investigate * Balances speed with accuracy + Moves quickly, but not at the expense of correctness. Knows when something is "good enough" versus when it needs deeper validation * Comfortable working through ambiguity + Can take an unclear question and structure it into a defined analysis without needing step-by-step direction * Finds the signal, not just the data + Doesn't stop at reporting. Identifies what actually changed and why it matters * Communicates simply and directly + Explains results in plain language. Avoids overcomplicating or hiding behind technical detail * Stays close to execution + Understands how campaigns are actually deployed and checks that analytics reflects reality * Flags issues early + Raises risks, inconsistencies, or gaps before they become problems in client settings, * Your Insights can lead to changes in targeting, spend, or program structure * Numbers are trusted; minimal rework or revalidation needed * Analysis is timely and moves decisions forward * Stakeholders rely on you to explain "why" not just "what happened" ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) - [Anomaly Detection - Using unsupervised Machine Learning for detecting anomalies in customer base](https://www.wearedevelopers.com/videos/6-anomaly-detection-using-unsupervised-machine-learning-for-detecting-anomalies-in-customer-base) ## Related Articles - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Data Analyst Salary Germany](https://www.wearedevelopers.com/magazine/277-data-analyst-salary-germany) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Analyst Salary in Switzerland](https://www.wearedevelopers.com/magazine/276-data-analyst-salary-in-switzerland)