Senior Data Engineer

About the Role As part of our economics transformation, we are reimagining how finance, business, and technology collaborate. Our teams are shifting to lean-agile, product-oriented delivery pods that integrate engineers, product owners, designers, data architects, and domain experts. Each pod owns outcomes end-to-end, with engineers contributing not only code but also to design reviews, backlog refinement, and retrospectives. The Senior Data Engineer plays a hands-on role in building high-performance data pipelines, integrations, and services that power analytics, AI/ML workflows, and enterprise-wide data products. This role is ideal for an experienced engineer who thrives in collaborative, agile environments and can balance rapid delivery with long-term scalability and governance. Key Responsibilities Design & Build Scalable Data Pipelines • Lead development of batch and streaming pipelines using AWS-native services (Glue, Lambda, Step Functions, Kinesis). • Implement best practices for monitoring, resilience, security, and cost optimization in high-scale pipelines. • Collaborate with architects to translate canonical and semantic models into physical implementations. Enable Analytics & AI/ML Workflows • Build pipelines delivering clean, structured data for BI tools, analytics, and ML pipelines. • Partner with data scientists to support feature engineering and deploy ML models into production. Ensure Data Quality & Governance • Embed validation, anomaly detection, and lineage into pipelines. • Contribute to the enterprise data catalog and enforce schema alignment across pods. • Implement role-based access controls, metadata standards, and tagging in collaboration with governance teams. Mentor & Collaborate Across Pods • Provide technical guidance to junior data engineers, sharing best practices in design and coding standards. • Participate in pod ceremonies (standups, backlog refinement, sprint reviews) and cross-program architecture syncs. • Advocate for reusable services and platform-first approaches to reduce fragmentation. Must-Have Qualifications • 7+ years of experience in data engineering with expertise in AWS services (Glue, Kinesis, Lambda, RDS, DynamoDB, S3). • Hands-on experience with orchestration tools such as Airflow or Step Functions. • Strong skills in SQL, Python, PySpark, and scripting for data transformation. • Experience working with modern data platforms (e.g., Snowflake, Databricks, Redshift, Informatica). • Proven ability to design and optimize pipelines for both batch and streaming use cases. • Knowledge of data governance practices including lineage, validation, and cataloging. Nice-to-Have Qualifications • Experience collaborating across matrixed, pod-based teams in agile environments. • Strong mentoring and collaboration skills with the ability to influence pods and domains. • Familiarity with large-scale AI/ML-driven workflows. Benefits: • This role is eligible to enroll in both Mondo's health insurance plan and retirement plan. Mondo defers to the applicable State or local law for paid sick leave eligibility.