Principal Data Engineer

About the role

Join a purpose-driven winning team, committed to results, in an inclusive and high-performing culture. The Principal Data Engineer will lead the design and evolution of enterprise-scale, multi-cloud data platforms and ingestion capabilities.

Responsibilities

• Architect and lead the implementation of enterprise-scale data ingestion frameworks and patterns capable of processing structured, semi-structured, streaming, and unstructured data across hybrid and multi-cloud environments.
• Define and implement reusable ingestion frameworks, reference architectures, and engineering standards that accelerate source onboarding and reduce delivery complexity.
• Lead the technical design and optimization of high-throughput batch, streaming, CDC (Change Data Capture), API, event-driven, and file-based integration patterns.
• Design and implement data pipelines for mission-critical workloads, ensuring scalability, resiliency, observability, security, and operational excellence.
• Solve complex data integration challenges involving SaaS platforms, databases, mainframes, data warehouses, real-time messaging systems, and third-party applications.
• Establish robust data quality, lineage, metadata, reconciliation, and governance controls throughout the ingestion lifecycle.
• Drive adoption of modern engineering practices including Infrastructure-as-Code, CI/CD, automated testing, GitOps, and DevSecOps.
• Define and implement platform observability strategies including monitoring, logging, alerting, performance tuning, and operational support models.
• Provide technical leadership, architectural guidance, and mentorship to engineering teams while driving engineering excellence across multiple initiatives.
• Evaluate emerging technologies and recommend strategies to improve scalability, reliability, performance, security, and cost optimization of enterprise data platforms.

Requirements

• 10+ years of experience designing and delivering large-scale enterprise data platforms.
• Proven experience architecting cloud-native and hybrid data solutions across Azure, AWS, and/or GCP.
• Deep expertise designing enterprise ingestion architectures supporting high-volume, high-velocity, and mission-critical data workloads.
• Experience establishing technical roadmaps, reference architectures, engineering standards, and platform operating models.
• Strong ability to lead complex technical discussions and influence senior stakeholders across business and technology organizations.
• Data Engineering & Integration Expert-level experience building resilient, scalable ETL/ELT, CDC, event-driven, streaming, and batch ingestion pipelines.
• Extensive experience integrating data from enterprise applications, SaaS platforms, APIs, relational databases, NoSQL databases, messaging platforms, data warehouses, and external partner systems.
• Strong understanding of data modeling, schema evolution, metadata management, data lineage, and data governance principles.
• Experience implementing end-to-end data quality frameworks, reconciliation processes, and operational controls.
• Deep expertise working with structured, semi-structured, and unstructured datasets at enterprise scale.
• Platforms & Technologies Expertise with modern Lakehouse architectures, including Databricks and Delta Lake.
• Strong experience with cloud-native data services across Azure, AWS, and GCP.
• Advanced programming skills in Python, SQL, Scala, and Java.
• Experience with distributed processing frameworks and large-scale data processing technologies.
• Experience with event-streaming and messaging technologies such as Kafka, Event Hubs, Pub/Sub, or equivalent platforms.
• Strong understanding of containerization, orchestration, microservices, and cloud-native architecture patterns.
• DevOps & Engineering Excellence Extensive experience implementing CI/CD pipelines using GitHub, Bitbucket, Azure DevOps, GitLab, Terraform, and Infrastructure-as-Code practices.
• Strong experience embedding security controls, compliance requirements, and automated testing into engineering pipelines.
• Demonstrated expertise in troubleshooting, root-cause analysis, performance optimization, and production support.
• Communication & Collaboration Ability to translate complex technical architectures into clear business outcomes for executive and non-technical audiences.
• Proven track record of leading cross-functional teams and delivering complex data programs in large enterprise environments.
• Strong mentorship and coaching capabilities with a passion for developing engineering talent.

Preferred Qualifications

• 5+ years of experience with Databricks and Lakehouse architecture.
• Experience supporting enterprise AI, machine learning, analytics, or real-time decisioning platforms.
• Experience designing multi-region, highly available, and disaster-resilient data platforms.
• Knowledge of enterprise governance, regulatory, privacy, and security requirements within highly regulated industries.