
Strategic Data Architect
2 weeks ago
As we embark on the next phase of growth, data will be at the heart of every strategic decision, driving product development, marketing initiatives, operations, and finance.
Key Responsibilities:- Lead the redesign and optimization of our data infrastructure to support large-scale analytics and informed decision-making.
- Define a unified framework for data creation, transformation, and consumption across the organization.
- Develop integrated data views by combining behavioral and transactional data into structured, query-optimized flat tables that cater to diverse analytics requirements.
- Own analytics across mobile, web, marketing, product, and operations data streams, ensuring consistency in data definitions and scalability.
- Collaborate with cross-functional teams to ensure data democratization and actionable insights.
- Establish robust data governance, quality standards, and best practices for the organization.
- Drive adoption of self-service analytics and empower teams with relevant dashboards, reporting, and insights.
- Introduce and scale ML-driven insights: Collaborate with product and engineering to enable use cases like personalization, predictive modeling, and anomaly detection.
- Build and mentor a high-performing analytics team spanning business/operations analytics, marketing/CRM analytics, and product/finance analytics.
- 8+ years in data & analytics leadership, with a proven track record in building data marts and defining enterprise-wide data consumption strategies.
- Strong expertise in running analytics across mobile, web, marketing, product, and operations data ecosystems.
- Deep technical knowledge in data modeling, flat table structures, and query optimization for large-scale systems.
- Hands-on experience with both behavioral and transactional data, and integrating them into a single analytics layer.
- Strong understanding of modern data stack (SQL, ETL/ELT, cloud data warehouses like Snowflake/BigQuery/Redshift, BI tools like Looker/PowerBI/Tableau).
- ML Competency: Familiarity with machine learning workflows (data pipelines for ML, feature engineering, model monitoring) and ability to guide data scientists in building predictive/ML models.
- Proven ability to build and scale analytics teams, with experience in functions such as business/operations, marketing/CRM, and product/finance analytics.
- Strong business acumen — ability to translate data into actionable insights for growth, retention, and efficiency.
- Excellent stakeholder management and communication skills, comfortable influencing at the CXO level.
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