Platform Engineer III

As a Data Platform Engineer III (Senior), you will design, build, and operate the foundational infrastructure that powers data-driven decisions across the organization. You will own complex technical problems end-to-end – from architecture and implementation to operations and reliability – while mentoring junior engineers and influencing cross-team technical strategy.

You will collaborate closely with analytics, machine learning, and product teams to evolve the platform to meet growing scale, compliance, and quality requirements. This is a high-impact, high-autonomy role for engineers who care deeply about data reliability, developer experience, and scalable system design.

• Design and implement scalable, reliable, and cost-efficient data platform components, including ingestion pipelines, storage layers, orchestration frameworks, and serving infrastructure.
• Lead architectural decisions for data systems, evaluate trade-offs, and document technical decisions (ADRs) for broader team alignment.
• Own the full lifecycle of platform components: design, build, test, deploy, monitor, and optimize.
• Drive adoption of platform-as-a-product principles, ensuring internal teams have self-service tooling, clear SLAs, and well-documented APIs.

• In collaboration with the data governance team, implement and maintain data observability tooling (data quality checks, anomaly detection, lineage tracking) to ensure trust in data assets.
• Define and enforce SLAs/SLOs for data pipelines and platform services; own incident response and root-cause analysis for platform-related outages.
• Establish and improve testing practices for data pipelines (unit, integration, and contract testing).

• Manage cloud data infrastructure (AWS, GCP, or Azure) using infrastructure-as-code tools such as Terraform, Pulumi, or CDK.
• Optimize platform cost, performance, and scalability through profiling, benchmarking, and capacity planning.
• Build and maintain CI/CD pipelines for data platform components; enforce engineering best practices (code review, versioning, documentation).

• Serve as a technical anchor for platform squads; mentor Level I and II engineers through code reviews, pairing sessions, and design feedback.
• Partner with data engineering, ML engineering, and analytics teams to understand requirements and translate them into platform capabilities.
• Represent the data platform team in cross-functional planning; contribute to roadmap prioritization and estimation.
• Contribute to and help maintain internal engineering standards, runbooks, and on-call practices.

• 5-8 years of professional software or data engineering experience, with at least 3 years focused on data platform, infrastructure, or distributed systems.
• Strong proficiency in Python and/or Scala/Java for building data pipelines and platform tooling.
• Deep working knowledge of a modern data warehouse or lakehouse platform (Redshift, Snowflake, Big Query, Databricks, or similar).
• Experience with workflow orchestration tools such as Apache Airflow, Prefect, or Dagster.
• Solid understanding of cloud infrastructure (AWS, GCP, or Azure) and infrastructure-as-code (Terraform or equivalent).
• Experience designing and building streaming and batch data pipelines at scale (Kafka, Kinesis, Pub/Sub, or equivalent).
• Proven ability to own complex systems end-to-end with minimal oversight; demonstrated technical judgment and decision-making.
• Strong communication skills; ability to explain technical concepts to non-technical stakeholders and write clear design documents.

• Open-source contributions to data tooling or active participation in the data engineering community.
• Knowledge of data modeling concepts (Kimball, Data Vault, OBT) and semantic layer frameworks (dbt, LookML).
• Experience with containerization and orchestration (Docker, Kubernetes, Helm).
• Background in ML infrastructure or MLOps (feature stores, model registries, training pipelines).
• Exposure to data governance, privacy engineering, or regulatory compliance (HIPAA).
• Prior experience…