Data Engineer

Requirements

• Exp- 3 to 7 years
• Strong SQL expertise with proven ability to write, optimize, and maintain complex queries
• Experience designing and implementing modern, architecture-based data warehouses in enterprise environments
• Hands-on experience with cloud data warehouses such as Snowflake, Redshift, or BigQuery
• Proficiency in scripting or software engineering languages such as Python, Bash, or JavaScript
• Strong understanding of data modeling, ETL/ELT design, and data lifecycle management
• Ability to work with large-scale datasets from diverse source systems
• Ability to communicate technical concepts clearly to both technical and non-technical stakeholders
• Additional strengths include: advanced Snowflake capabilities, experience with cloud-native data services, orchestration tools (e.g., Airflow, Spark, Kafka), data governance frameworks, time-series analytics, and familiarity with security or compliance requirements related to enterprise data.
• This commitment applies to all aspects of employment, including recruitment, hiring, placement, promotion, termination, layoff, recall, transfer, leave, compensation, and training.
• If you require a disability-related or religious accommodation during the application or recruitment process, and wish to discuss possible adjustments, please contact jobs@invoicecloud.com .
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Additional Information

About InvoiceCloud : InvoiceCloud is a fast-growing fintech leader recognized with 20 major awards in 2025, including USA TODAY and Boston Globe Top Workplaces, multiple SaaS Awards wins for Best Solution for Finance and FinTech, and national customer service honors from Stevie and the Business Intelligence Group. Judges also highlighted our mission to reduce digital exclusion and restore simplicity and dignity to how people pay for essential services, as well as our leadership in AI maturity and responsible innovation. It's an award-winning, purpose-driven environment where top talent thrives. To learn more, visit InvoiceCloud.com . Job Details The Data Warehouse Engineer designs, builds, and optimizes scalable data pipelines that connect solution-area source systems to InvoiceCloud's enterprise data warehouse. This role focuses on enabling reliable analytics, data science, and reporting by developing automated workflows, improving performance, and maintaining strong data quality standards. You will work closely with technology teams and business stakeholders to translate operational and analytical requirements into durable data warehouse solutions that support enterprise decision-making. Success Profile At InvoiceCloud, success is anchored in our core competencies. These competencies guide how every employee delivers impact across their role Results Driven Delivers reliable, scalable data pipelines that support analytics, reporting, and data science initiatives. Translates business and operational requirements into effective warehouse designs and data models. Diagnoses and resolves issues in high-volume data processing systems to minimize downtime and protect data integrity. Ensures warehouse architecture supports performance, accuracy, and enterprise analytics needs. Takes Ownership Designs, develops, and maintains automated, real-time data pipelines from multiple source systems into the data lake and warehouse. Maintains deep understanding of source systems and downstream consumers to champion data usability and reliability. Implements auditing, logging, and data quality controls to ensure consistency and trust in data workflows. Owns issue resolution across ingestion, transformation, and delivery layers, coordinating with partners as needed. Drives Efficiency Optimizes pipeline and query performance using SQL, Python, profiling tools, and tuning techniques. Implements scalable Snowflake development workflows that reduce rework and accelerate delivery. Applies strong ETL/ELT design principles to streamline data movement and lifecycle management. Participates in architecture reviews and contributes to best practices that improve warehouse scalability and maintainability. Innovative Researches and adopts emerging technologies and modern architectural patterns to improve performance and scalability. Enhances warehouse capabilities to better support advanced analytics and data science initiatives. Experiments with new orchestration, automation, and monitoring approaches to improve reliability and observability. Contributes to evolving data governance standards and quality frameworks across the organization.

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