Senior Data Engineer

Senior Data Engineer Position open to applicants based in the Philippines only. Job Scope The Senior Data Engineer is a key contributor in building and maintaining robust, secure data infrastructure for internal and external clients. This role combines deep technical expertise, architectural knowledge, and AI/ML engineering skills to design scalable data systems for client engagements and optimize internal data platforms with a strong emphasis on data governance, quality assurance, and privacy protection. The engineer collaborates closely with client teams and internal stakeholders, develops comprehensive data architectures, and acts as a subject matter expert in implementing secure, compliant data engineering solutions across both client projects and internal systems while ensuring the highest standards of data protection and quality. Key Responsibilities Client‑Facing Delivery Lead the design, development, and implementation of scalable data pipelines and ETL/ELT processes for client engagements, delivering business intelligence and advanced analytics solutions with robust data quality controls Collaborate directly with external clients to understand their data requirements and translate business needs into secure, compliant technical data engineering solutions Ensure all client data solutions adhere to strict data governance frameworks and privacy protection standards Internal Infrastructure & Data Management Build internal data infrastructure from scratch or integrate with existing client systems, including secure data warehouses and data lakes that support organizational operations and analytics capabilities Design and implement comprehensive data models for both client projects and internal systems, ensuring optimal performance, scalability, and data integrity Develop and maintain data ingestion processes and APIs to seamlessly integrate data from multiple sources while maintaining data quality and security Data Governance & Security Establish and enforce comprehensive data privacy protocols and data governance frameworks to ensure compliance with regulatory requirements and protect sensitive client data Implement automated data quality monitoring systems to proactively identify and resolve data integrity issues across all data pipelines Design and maintain data lineage tracking and audit trails to ensure full transparency and accountability in data processing workflows Apply security best practices including data encryption, access controls, and secure data handling procedures throughout all data engineering processes Technology Integration & Innovation Integrate AI/ML technologies (e.g., automated data processing, intelligent data validation, predictive data quality monitoring, and ML‑powered analytics pipelines) into client deliverables and internal workflows Leverage cloud-based platforms including Snowflake and Databricks to build scalable, cost‑effective, and secure data solutions Stay current with emerging data engineering technologies and data governance best practices to enhance both client outcomes and internal capabilities Qualifications Bachelor's degree in Physics, Engineering, Computer Science, Information Systems, Mathematics, or a related technical field At least 3 years of experience in data engineering, with proven experience building data pipelines for business intelligence and advanced analytics Strong business acumen with experience working directly with external clients and internal cross‑functional teams Demonstrated track record of delivering complex data engineering projects that drive measurable business impact while maintaining data security and quality standards Excellent communication skills, both verbal and written, with the ability to present technical concepts to non‑technical stakeholders and clients Experience with data privacy regulations and governance frameworks is essential Experience in both client‑facing and internal environments is a plus Accounting, finance, or manufacturing domain knowledge is a plus Technical and Other Skills Expert-level proficiency in SQL for complex data extraction, transformation, and modeling Advanced programming skills in Python and SQL for data pipeline development and automation Experience with data orchestration tools (e.g., Apache Airflow, dbt) and workflow management systems Skilled in API development and integration, including RESTful APIs and data ingestion from various sources Extensive experience with cloud data platforms, specifically Snowflake and Databricks, for building enterprise‑scale data solutions Strong expertise in data modeling techniques and tools for designing efficient database schemas and data warehouse architectures Deep understanding of ETL/ELT processes, data warehousing concepts, and modern data architecture patterns Understanding of CRM/ERP systems (e.g., QuickBooks, Xero, NetSuite, SAP) and experience integrating them into data pipelines Familiarity with containerization technologies (Docker, Kubernetes) and cloud services (AWS, Azure, GCP) Familiarity with version control systems (Git) and CI/CD practices for data engineering workflows Experience with Agile/Scrum methodologies and project collaboration tools Understanding of data governance frameworks, data privacy regulations (GDPR, CCPA), and security best practices for handling sensitive financial data Experience implementing data quality frameworks, data lineage tracking, and automated data validation processes Knowledge of data encryption, access control mechanisms, and secure data handling procedures Seniority Level Mid‑Senior level Employment Type Full‑time Job Function Information Technology, Engineering, and Consulting Industries Professional Services, Banking, and Accounting #J-18808-Ljbffr