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Roles & Responsibilities
As a Senior Analytics Engineer, you will lead efforts to develops calable data solutions that enhance feature-level insights, user engagement measurement, and decision-making for the Product Analytics team. You will build and optimize data pipelines, semantic layers, and dashboard sto deliver actionable insights on howusers interact with in-app features, content discovery, and engagement-driving elements.
Design and implement scalable data models that support flexible querying,
feature attribution, and
behavioral analytics for self-serve and advanced analytics.
Partner with Product Analytics stakeholders to understand business needs and develop robust analytical solutions, focusing on
in-app engagement, navigation, and personalization signals.
Strong understanding of SDKs and event stream data to craft
analytics instrumentation proposals for new features, ensuring robust data collection and alignment with business objectives.
Advance automation efforts, enabling the team to spend less time on manual data validation and more on insights generation.
Leverage machine learning & statistical modeling techniques to enhance feature performance tracking, user segmentation, and predictive analytics.
Develop frameworks that multiply the productivity of the team and are intuitive for other data teams to leverage.
Optimize data processing pipelines and ETL workflows, ensuring data accuracy and consistency across multiple sources.
Improve experimentation infrastructure by enabling better A/B test measurement, control-group analysis, and impact assessments.
Set goals and targets for Core Product OKRsby leveraging advanced
data science methodologies, machine learning models, and statistical forecasting techniques to drive product engagement and retention strategies.
Create systematic solutions for solving data anomalies, including anomaly detection, root cause analysis, and proactive alerting.
Work with cross-functional teams, including data science, personalization, and content teams, to drive
data governance, experimentation analytics, and dashboarding strategies.
Identify and explore new opportunities through creative analytical and engineering methods, ensuring robust and scalable solutions for evolving business needs.
Technical Skills
5+ years of experience in analytics engineering, data science, or data engineering, preferably in
streaming, digital products, or consumer analytics.
Advanced SQL skills with expertise in writing
clean, efficient queries for large-scale analytics.
Experience in designing scalable data models(e.g., feature attribution, user segmentation, behavioral cohorts).
Proficiency in Python for data manipulation, automation, and statistical modeling.
Experience with machine learning techniques, such as
clustering, regressions, decision trees, and classification models to enhance feature-level insights.
Experience with big-data technologies, such as
Spark, Hive, Kafka, Airflow, or Snowflake.
Advanced experience with BI tools, such as
Looker, Tableau, or Mode, for building interactive dashboards and self-serve analytics solutions.
Familiarity with A/B testing frameworks and experimentation methodologies for measuring product feature performance.
Experience working with event-based data and optimizing data pipelines for
behavioral tracking and feature usage analysis.
Strong understanding of statistical analysis, such as hypothesis testing, significance testing, and probability distributions.
Business & Product Analytics Skills
Deep understanding of streaming product analytics, including
content engagement, recommendation effectiveness, and user navigation trends.
Experience in feature attribution modeling, enabling accurate assessment of how product features drive engagement.
Knowledge of user segmentation frameworks, including habituality, content diversity, and personalization effectiveness.
Ability to define and track product success metrics, identifying trends in
feature adoption, engagement drivers, and content discovery pathways.
Soft Skills
Strong written and verbal communication skills, with the ability to translate complex findings into actionable insights.
Ability to manage multiple projects, working cross-functionally across data engineering, analytics, and product teams.
Strong organizational skills, capable of acting independently while managing competing priorities.
Proactive problem-solver, with a keen eye for detail and a passion for improving self-serve analytics.
