Competitive Data Engineering Manager

Design, build, and maintain scalable data pipelines to ingest, transform, and integrate third-party/vendor datasets into the data warehouse.

Establish robust ETL/ELT frameworks to ensure data quality, reliability, and timeliness

Partner with Data Engineering teams to optimize data models, storage, and compute efficiency.

Define data schemas, validation checks, and monitoring systems to ensure high data integrity.

Reduce manual data handling by building automated ingestion and transformation workflows.

Own end-to-end onboarding of external/vendor data sources—from scoping requirements to pipeline deployment.

Manage vendor relationships and ensure consistent, high-quality data delivery.

Standardize and unify disparate data sources into a coherent and scalable data model.

Develop automated dashboards and reporting layers using Tableau (or similar BI tools).

Enable self-serve analytics by creating reusable data models and semantic layers.

Improve performance and usability of dashboards through optimization and standardization.

Implement AI-driven workflows (e.g., LLMs, internal tools) to automate data processing, analysis, and reporting.

Build and deploy automated pipelines and agents for competitive intelligence generation.

Continuously identify opportunities to reduce manual work and improve efficiency.

Collaborate across time zones with strong communication and execution discipline.

Support both long-term strategic projects and fast-turn ad hoc requests.

Identify scalability bottlenecks and propose technical solutions.

Establish best practices for data governance, pipeline reliability, and analytics workflows.

Provide technical guidance on feasibility, timelines, and data methodologies.

Successfully builds and scales reliable data pipelines for external data ingestion.

Enables automated, self-serve competitive analytics across the organization.

Reduces manual effort through strong automation and system design.

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