Designation: Assistant Manager
Level: L3
Experience: 5 to 8 years
Location: Chennai, Tamil Nadu, India.
Job Description
Position Overview
This high-impact role blends advanced data science and data pipeline engineering with strategic business engagement. You own the integrity of the end-to-end data journey—from audience contactability and growth to complex web data stitching logic and experimental measurement. Adhering to coding best practices & standards, this role turns complex requirements and methodologies into simple and usable features. You will anchor our analytics infrastructure across a complex, multi-market European landscape and ensure our backend data engineering logic is optimized for our current QlikSense environment, while leading the strategic technical transition to Power BI. Operating within an Agile framework (JIRA), you will ensure our data products are scalable, technically robust, and operationally relevant for National Sales Companies (NSCs) and localized market representatives across Europe.
Key Responsibilities
1. Data Architecture, Pipeline Modernization & Visualization Strategy
- Own and optimize the end-to-end data pipelines within Google Cloud Platform (GCP) and BigQuery, specifically building new pipelines and leading the migration from legacy queries to a managed Dataform environment.
- Develop and maintain robust data transformation logic using SQL and BigQuery Dataform, ensuring all data pipelines are version-controlled via Git and adhere to ""best-in-class"" style guides.
- Visualization Migration: Lead the technical roadmap for transitioning the department’s front-end suite from QlikSense to Power BI, ensuring that data models are refactored for performance and accuracy in the new environment.
- Act as the technical lead for a suite of data products, ensuring backend architecture is scalable, future-proof, and optimized for front-end visualization.
- Role-model, establish and encourage team-wide coding standards and documentation practices to reduce technical debt and improve deployment speed.
2. Advanced Experimentation & KPI Framework
- Lead the technical implementation to visualize Hypothesis Testing and A/B Testing Analytics capabilities within internal dashboarding tools.
- Translate theoretical statistical models into functional analytics features that allow marketing teams to measure the incremental impact of their experiments.
- Utilize Python to support advanced modeling, predictive analytics.
3. Analytics SME & Data Integrity (The ""Enforcer"")
- Serve as the de facto Subject Matter Expert (SME) for all things Analytics
- Identify and resolve critical data gaps by working directly with upstream ingestion teams, conducting rigorous User Acceptance Testing, and ensuring data accuracy.
- Collaborate with and hold external partners and internal stakeholders accountable for data quality, ensuring the team has the high-integrity tracking required to meet audience growth targets.
4. Team Acceleration & Agile Leadership
- Agile Execution: Manage work delegation and project workflows through JIRA, utilizing Agile methodologies to ensure transparent planning, backlog prioritization, and timely delivery of analytics features.
- Technical Mentorship: Coach and mentor junior data scientists and analysts to elevate the collective technical capability of the Marketing Analytics department.
- Operational Excellence: Proactively identify opportunities to eliminate ""waste"" in technical processes, using JIRA insights to identify bottlenecks and advocating for work that should be paused or automated.
Required Qualifications & Skills
- Cloud & Data Engineering: Expert-level SQL and deep experience with GCP (BigQuery, Dataform) and Git.
- Visualization & BI: Proficiency in QlikSense (Set Analysis, QVD generation) and Power BI (DAX, Power Query). Proven experience in leading or supporting a dashboard platform migration is highly desirable.
- Data Science & Statistics: Proven ability to produce/visualize post-experiment analytics of A/B testing and Hypothesis testing frameworks; mathematical foundation in experimental design beneficial therefore
- Programming: Proficiency in Python is required for advanced modeling and pipeline automation.
- Technical Communication: Proven ability to translate complex data science concepts into clear feedback for agencies and actionable insights for marketing stakeholders.
- Leadership: Experience in mentoring junior staff and driving the adoption of standardized development practices (Style Guides, Documentation).
- Agile & Project Management: Proficiency in JIRA and experience working within Agile/Scrum or Kanban frameworks to manage complex, multi-stakeholder project lifecycles.
- Experience: Significant experience in data science or technical analytics within a complex, multi-market European environment (Automotive experience is a plus).
