Data Engineering Manager

About Opptra: Opptra (www.opptra.com) is revolutionizing global expansion for consumer brands with a focus on ecommerce and digital capabilities. We're building a portfolio of category-specialized franchising businesses, powered by our centralized technology platform and global supply chain infrastructure. We create market access through franchising businesses that serve as master franchisees or licensing partners for brands entering new markets. Unlike traditional distribution partners that prioritize brick-and-mortar channels, our businesses leverage advanced ecommerce expertise to accelerate market entry while balancing online and offline channels to match local consumer behavior. With 70% of global consumer growth driven by Asia, we're currently focused on enabling access to these high-potential markets. Our model offers brands significant advantages: * Reduced market entry costs * Broader consumer reach * Faster testing and learning capabilities than traditional retail * Local expertise with global backing

Role Overview:

Opptra operates across multiple geographies, brands, and marketplaces, with complex operational flows spanning inventory, supply chain, performance marketing, pricing, and working capital management. We are looking for a Data Engineering Manager who will own the end-to-end data foundation powering business dashboards, automation systems, and AI/ML use cases. This role is not limited to building pipelines — it requires strong stakeholder management, business prioritization, sprint execution, and the ability to translate ambiguous business problems into scalable data systems. You will act as the connective tissue between business teams and engineering execution, ensuring data infrastructure directly drives measurable business impact

Core Responsibilities:

Data Platform Ownership :

Design, build, and scale Opptra’s data warehouse architecture (multi-region, multi-marketplace) Own ingestion pipelines from OMS, WMS, marketplaces, performance marketing platforms, finance systems, and external web data sources. Build robust, reliable, and well-documented data models for analytics and AI use cases. Ensure data quality, validation, monitoring, and lineage.

Business-Facing Analytics & Dashboards

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Translate business requirements into clearly defined metrics and dashboards.

Deliver high-impact dashboards across : Performance marketing (RoAS, CAC, contribution margin) Sales & channel performance Inventory health & working capital Forecasting & buying Ensure dashboards drive decisions, not just reporting. Establish metric definitions and governance to avoid ambiguity

API & Systems Integration

Build and manage API connectors across: Marketplaces OMS/WMS systems Marketing platforms Internal automation tools

Enable structured data availability for AI/ML models and automation workflows. Work closely with product and AI teams to ensure clean, consumable data layers.

AI-Readiness & Advanced Use Case Enablement Architect data systems to support forecasting, pricing optimization, inventory intelligence, and AI-driven automation. Partner with AI/FDE teams to ensure low-friction data access Support experimentation frameworks and feedback loops.

Stakeholder Management & Cross-Functional Alignment Work directly with category leaders, supply chain, finance, marketing, and founders to clarify requirements. Proactively manage expectations and communicate timelines Push back constructively when asks are unclear, conflicting, or misaligned

Business Ask Prioritization Own backlog grooming and sprint planning Evaluate requests based on impact, effort, and strategic alignment. Establish a structured intake and prioritization framework. Ensure high-velocity delivery without sacrificing system stability.

Execution Discipline Run structured sprints with clear timelines and deliverables Deliver visible outputs within defined cycles. Provide weekly progress updates to leadership. Identify bandwidth gaps and proactively recommend hiring/contracting needs

Team Leadership Hire, mentor and guide data engineers. Create a culture of ownership, documentation, and operational rigor Ensure clarity of roles and accountability within the team.

Required Qualifications & Experience 7–10+ years of experience in data engineering, analytics engineering, or data platform roles. Proven experience building and scaling data warehouses (Snowflake/BigQuery/Redshift or equivalent). Strong hands-on experience with ETL/ELT pipelines, API integrations, and data modeling. Experience building business-facing dashboards (Looker, Tableau, Power BI, or equivalent). Experience working in e-commerce, marketplaces, supply chain, or inventory-led businesses. Demonstrated ownership of sprint planning and execution in cross-functional environments