We're looking for an exceptionally skilled and influential Principal Data Engineer / Data Lead to provide technical leadership for a digital solution focused on cervical screening. This is a critical leadership role for someone passionate about data, delivery, and technical excellence. As Data Lead, your primary focus will be shaping the strategic direction and delivery of data-driven features and orchestrating data capabilities across cross-functional agile teams.

🎯 Responsibilities

1. Lead data integration and adoption of data fabric across domains, bringing together data, business, and development.
2. Ensure best practices and support organization-wide standards for data quality, governance, security, and observability.
3. Act as a technical subject matter expert, mentoring Senior and Staff Engineers, and collaborating closely with engineering leadership, data scientists, and business stakeholders.
4. Shape data strategy in partnership with cross-functional stakeholders and translate business needs into scalable technical solutions.
5. Drive the evaluation and integration of new technologies to continuously improve the data ecosystem's performance and reliability.
6. Troubleshoot and resolve complex performance and architectural challenges within the data environment.
7. Define and ensure timely delivery of data-related requirements, including integration, migration, storage, structure, legal compliance, privacy, quality, and insights generation.
8. Orchestrate Data Analytics (DA) engagement and delivery for digital products, activating appropriate DA capabilities.
9. Build and maintain strong working relationships with cross-functional experts (data experts, product owners, software engineers, clinicians) to explore business opportunities and data assets.

🛠️ Requirements

1. 8+ years of professional experience in a Data Engineering role, including significant time at a Principal or Staff level.
2. Expert proficiency in SQL and Python.
3. Extensive experience architecting and managing large-scale cloud data platforms (such as AWS Glue, Google Dataflow, Azure Data Factory) and deep expertise in containerization and orchestration (Docker/Kubernetes).
4. Strong hands-on experience in data warehousing, advanced modeling techniques (dimensional modeling, data vault), and establishing data governance frameworks.
5. Proven ability to build, optimize, and lead deployment of large-scale, highly concurrent data pipelines using distributed processing frameworks (e.g., Apache Spark, Flink).
6. Exceptional communication, negotiation, and influencing skills, able to present complex technical concepts to both technical and executive audiences.

➕ Nice to have

1. Good command of Spanish.
2. Experience with workflow orchestration tools (e.g., Apache Airflow, Dagster) and management at scale.
3. Experience with NoSQL and graph databases (e.g., MongoDB, Cassandra, Neo4j).
4. Expertise in stream processing architectures and technologies (e.g., Kafka, Kinesis, Pulsar).
5. Extensive experience with Terraform, CloudFormation, or other advanced Infrastructure as Code (IaC) tools and GitOps methodologies.