Senior Data Engineer

Description

• Collaborate with platform, DevOps, and architecture teams to build secure, reusable, and CI/CD-enabled data workflows that align with enterprise architecture standards.
• Monitor and fine-tune data pipeline and query performance, optimizing for reliability, scalability, and cost-efficiency.
• Design and execute comprehensive testing strategies—unit, integration, and system testing—ensuring high data reliability and pipeline resilience.
• Lead the end-to-end design and development of scalable, high-performance data pipelines and ETL/ELT frameworks aligned with modern data engineering best practices.
• Ensure optimal pipeline performance and throughput, with clearly defined SLAs and proactive alerting for data delivery or quality issues.
• Facilitate and lead discussions with business and operational teams to understand data requirements, prioritize initiatives, and drive data strategy forward.
• Create and maintain detailed technical documentation, including data architecture diagrams, process flows, and integration specifications for internal and external stakeholders.
• Architect complex data integration workflows that bring together structured, semi-structured, and unstructured data from both cloud and on-premise sources.
• Partner with analysts, data scientists, and business leaders to define and deliver reliable data models, quality frameworks, and SLAs that power key business insights.
• Guide the team in establishing standards for data modeling, transformation logic, and governance, ensuring long-term maintainability and scalability.
• Lead root cause analysis (RCA) and preventive maintenance for critical data failures, ensuring minimal business impact and continuous service improvement.
• Establish and enforce the best practices in source control, code reviews, testing automation, and continuous delivery for all data engineering components.
• Build robust real-time, batch, and on-demand pipelines with built-in observability—monitoring, alerting, and automated error handling.

Requirements

Equivalent academic and work experience can be considered.

Bachelor’s or Master’s degree in Computer Science/Information technology or related field.

• 4+ years of experience with Snowflake, including performance tuning, security model design, and advanced SQL development.
• 5+ years of experience in writing complex, highly optimized SQL queries on large datasets.
• Strong interpersonal and communication skills with the ability to lead discussions with technical and business stakeholders.
• Experience with CI/CD pipelines, automated testing, and code versioning tools (e.g., Git).
• Proven experience integrating and managing APIs, JSON, XML, and webhooks for data acquisition.
• 7 to 10 years of hands-on experience in data engineering roles with a proven record of building scalable and secure data platforms.
• Hands-on experience with cloud platforms (Azure/AWS) and orchestration tools like Apache Airflow or equivalent.
• 5+ years of experience with data integration tools such as Azure Data Factory, Fivetran, or Matillion.
• Over 5 years of experience in scripting languages such as Python for data processing, automation, and ETL development.
• Familiarity with dbt or similar transformation tools and best practices for modular transformation development.
• Exposure to data visualization tools like Power BI for supporting downstream analytics is a plus.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance