Technical Product Manager - Tracking Data (d/f/m) fixed term (1 year)

Das ist der Job

Strong hands‑on understanding of web/app analytics and measurement design.

Darum lohnt es sich

Proven ability to run roadmaps/backlogs, drive delivery, and align multiple teams. Nice‑to‑have Experience with tools like GA/Adobe Analytics, GTM (incl. server‑side), cloud/CMP/consent tooling, experimentation platforms, and modern data tooling (dbt, Airflow/Dagster, BigQuery/Snowflake/Databricks).

Drive timely decisions, unblock teams, manage dependencies, and keep multiple groups aligned. The team saves time through focused automation (QA, documentation, triage), improving reliability and responsiveness. Job benefits Rewards : competitive base salary and participation in the company’s incentive programmes.

Team‑Building : regular team and company events to network and learn. Enjoy working : well‑equipped office supports focused work and teamwork, with social zones.

Technical Product Manager – Data Analytics & Tracking (d/f/m) - fixed term (1 year) You ensure continuity and operational excellence for the measurement and tracking product across the business: tracking instrumentation (web/app), data collection, consent‑aware enrichment, transformation & ETL/ELT, and the reliable delivery of analytics‑ready datasets to stakeholders.

You sit at the center of Product, Analytics, Data Engineering, and MarTech—turning business needs into clear requirements, coordinating delivery, and ensuring the end‑to‑end system runs reliably. Your focus is on stability, quality, and smooth execution of the established roadmap, while driving incremental improvements where they add clear value.

Job requirements Must-have 5+ years experience in technical product management, analytics engineering, data engineering, digital analytics, or similar—ideally in e‑commerce/marketplace environments. Strong understanding of ETL/ELT, transformation modelling, and the end‑to‑end lifecycle of data products.

Very good SQL skills and confidence working across complex, multi‑source data systems. Excellent communication skills and a strong sense of ownership—reliable in execution and comfortable coordinating across functions. Experience implementing monitoring/data quality frameworks and data observability tooling.

Experience introducing AI productivity solutions responsibly (governance, access control, evaluation). Job responsibilities 1) Product management & ownership (continuity & execution) Drive day‑to‑day product management for tracking and downstream datasets, ensuring reliable execution of the existing roadmap and priorities.

Track success metrics and operational targets (e.g., coverage, accuracy, latency/freshness, adoption, SLA/SLO), and ensure risks are visible early. Write and refine requirements: epics, user stories, acceptance criteria, and Definition of Done to support efficient delivery.

Support pragmatic decision‑making and trade‑offs across value, effort, risk, and compliance—keeping delivery moving and data trusted. 2) Digital analytics tracking & instrumentation (web/app) Maintain the established event taxonomy and measurement framework (events, parameters, conversions, funnels, identity signals).

Translate business outcomes into implementable tracking specs and coordinate rollout with Engineering. Ensure consistent standards: naming conventions, reusable components, versioning, and controlled change management.

Safeguard tracking quality across releases through validation, testing, and structured rollout practices. 3) MarTech measurement & marketing data alignment Ensure continuous measurement across the marketing stack (tags/pixels, server‑side tagging, campaign parameters, conversion APIs where applicable).

Maintain alignment of conversion and KPI definitions across Product Analytics and Marketing reporting to reduce metric drift and duplicated logic.

Coordinate with Growth/Marketing, Privacy/Legal, and Engineering to keep measurement compliant and effective. 4) Data engineering delivery: transformation, ETL/ELT & semantic modelling Coordinate delivery of curated datasets powering dashboards, experimentation, and deep‑dive analysis, including: ingestion validation from tracking + MarTech sources transformations (sessionisation, identity stitching, channel grouping, conversion modelling) semantic layers / metric standardisation for self‑service BI Maintain data contracts between tracking schemas and warehouse models and ensure changes are introduced safely.

Partner with Data Engineering to keep pipelines reliable, scalable, and cost‑efficient; support continuous improvements when needed. 5) Monitoring, data quality & incident management (operational excellence) Own observability practices across tracking and pipelines: completeness, freshness, anomaly detection, schema drift, and KPI sanity checks.

Lead incident handling end‑to‑end: triage, comms, coordination, RCA, post‑mortems, and prevention follow‑ups.

Keep runbooks and operational playbooks current; ensure transparent stakeholder updates and predictable processes. 6) AI productivity & automation (practical improvements) Drive pragmatic AI‑enabled improvements that reduce manual effort and increase reliability, such as: automated tracking QA (event validation, regression detection, release gates) AI‑assisted documentation (event catalog, metric definitions, change logs) anomaly explanation and incident triage support self‑service support for stakeholders (metric guidance, lineage/Q&A) automation for backlog hygiene and requirement drafting Ensure AI features meet quality and compliance expectations: access controls, privacy, auditability, and evaluation criteria. 7) Leadership, decision‑making & ways of working Provide strong day‑to‑day leadership for the cross‑functional squad (tracking implementation + analytics engineering + MarTech interfaces).

Run effective delivery routines: prioritisation, rituals, Definition of Ready/Done, quality gates, and communication cadence.

Support a culture of ownership, quality, and continuous improvement. 8) Stakeholder management & communication Be the primary point of contact for Product Managers, Engineering leads, Analysts, Data Scientists, and Marketing stakeholders for tracking operations and delivery topics.

Communicate proactively: roadmap progress, release notes, known limitations, trade‑offs, and timelines. Enable self‑service through training, office hours, and well‑maintained documentation (event catalog, KPI glossary, dataset guides).

What success looks like Tracking and datasets remain stable, trusted, and well‑documented, with strong monitoring and predictable operations. Measurement changes are delivered smoothly through clear standards, testing, and disciplined change management.

Product and Marketing stay aligned on KPI definitions, enabling faster decisions and fewer reporting discrepancies. Plan for the Future : on top of statutory retirement insurance, competitive company pension scheme. Learning & Development : access to coaching, learning platforms and resources. Mobility : monthly transportation allowance.

Flexible working : hybrid approach, option to work abroad for a set number of days per year. Wellbeing : confidential employee assistance and support services for employees and families. Family support : enhanced parental leave and family‑friendly policies. mobile.de is an equal‑opportunity employer and we value diversity.

We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status or disability status. #J-18808-Ljbffr