Lead Data Engineer

Work Schedule

Environmental Conditions

• Lead the build, development, and optimization of scalable, secure, and high-performance data pipelines and architectures.
• Lead and guide a team of data engineers and analysts, encouraging teamwork and technical excellence.
• Implement standard methodologies for DataOps across the platform to uphold 99.99%+ availability, reliability, and recovery standards.
• Build and maintain robust data models and frameworks, ensuring they align with enterprise architecture and governance standards.
• Collaborate with cross-functional teams, including analytics, product, and business units to deliver high-impact, data-driven solutions.
• Ensure data quality and validation through automated testing, monitoring, and anomaly detection techniques.
• Own planning, prioritization, and execution of data engineering projects — delivering on time and within scope.
• Contribute to platform scalability, performance tuning, and migration initiatives, including support for real-time data flows.
• Deliver technical documentation and code reviews to uphold engineering standards and maintain knowledge continuity.
• Support data privacy, security, and compliance requirements in line with global regulatory standards.
  

Qualifications

• Bachelor's degree or equivalent experience in Computer Science, Data Engineering, Information Systems, or a related field.
• Proven experience (5+ years) in data engineering with a strong understanding of modern data architecture, pipelines, and operations.
• Expertise in ETL/ELT tools, data warehousing, and cloud-native platforms such as Databricks, Snowflake, or BigQuery.
• Strong hands-on experience with SQL, Apache Spark, and Python (experience with Pandas preferred).
• Proficient in working with large datasets and performance optimization for distributed computing environments.
• Prior experience with tools like Informatica, Cognos, SQL Server, and Oracle is a strong plus.
• Deep understanding of data modeling, metadata management, and data governance frameworks.
• Demonstrated experience in leading engineering teams and managing project lifecycles.
• Familiarity with DevOps/DataOps practices, version control systems (e.g., GitHub), and CI/CD pipelines.
• Excellent communication, leadership, and collaborator management skills.
• Experience working in cloud environments such as AWS, Azure, or GCP.