Data Analysis Specialist

Company description Publicis Global Delivery is the talent powerhouse of Publicis Groupe, the largest global communications group. We make sure to hire, boost and develop the best people worldwide to deliver outstanding work for the most prominent clients within the Groupe. In LATAM, we are over 1,700 passionate employees that love to push boundaries and drive innovative solutions. If you are a risk-taker and love to develop intrepid ideas, PGD is the place for you. We Move People, and People Move Us! Overview The Data Analysis Specialist 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 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 Qualifications Required qualifications English level C1 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 Python strong experience Experience managing multiple databases and handle a high volume of requests under pressure, while maintaining accuracy and prioritization. Experience managing multiple points of contact across cross‑functional teams, balancing priorities and maintaining alignment in complex environments. Experience with Databricks, Python, or R for deeper analysis Preferred qualifications Experience in automotive, CRM, or lifecycle marketing Exposure to test/control design and incrementality measurement Familiarity with Tableau or similar visualization tools (used for communication, not just reporting)

Required qualifications English level C1 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 Python strong experience Experience managing multiple databases and handle a high volume of requests under pressure, while maintaining accuracy and prioritization. Experience managing multiple points of contact across cross‑functional teams, balancing priorities and maintaining alignment in complex environments. Experience with Databricks, Python, or R for deeper analysis Preferred qualifications Experience in automotive, CRM, or lifecycle marketing Exposure to test/control design and incrementality measurement Familiarity with Tableau or similar visualization tools (used for communication, not just reporting)

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 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