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. Key Responsibilities Platform Architecture & Engineering 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. Data Reliability & Quality 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). Infrastructure & Operations 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). Collaboration & Technical Leadership 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, BigQuery, 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. Preferred Qualifications 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 leading technical projects or acting as a tech lead on a data platform team. Open-source contributions to data tooling or active participation in the data engineering community

Education:

Minimum of HS Diploma or GED Bachelors preferred