Staff Data Platform and Products Architect

Jobtailor; Dearborn (MO); On-site; USD 140,000 - 190,000; Full time

Job summary

Jobtailor in Dearborn, MI seeks a Senior Data Platform Engineer to design, build, and govern scalable data solutions on Google Cloud. You will lead API design, data governance, and IaC practices to enable product teams across the platform.

You will mentor engineers, develop reusable frameworks, and drive platform strategy for analytics, streaming, and ML pipelines, collaborating with business and engineering leaders in a hybrid work environment.

Qualifications

• Bachelor's degree in Computer Science or related field.
• 7+ years of data architecture or platform engineering experience.
• 3+ years designing data platforms on cloud (GCP/AWS/Azure).
• Proficient in Python, SQL, and Java (or Go).
• Experience building centralized developer ecosystems or templates.
• Hybrid work requirement: reside within commuting distance of Dearborn, MI.

Responsibilities

• Architect scalable data platform solutions on GCP, leveraging modern data architectures.
• Lead cloud data integration and streaming pipelines for real-time and batch workloads.
• Design and productize secure, versioned RESTful and gRPC APIs; define API contracts and auth patterns.
• Enforce schema-first governance with Protobuf and central registries; ensure backward compatibility.
• Build reusable platform frameworks and templates to accelerate pipelines and microservices.
• Drive platform and data strategy by aligning with business and engineering leaders.
• Automate IaC and CI/CD pipelines (Terraform, GitHub Actions, Tekton).
• Model and transform data across analytical and operational stores; orchestrate Spark pipelines.

Skills

Data architecture

Platform engineering

GCP expertise

Python/SQL/Java

APIs design

IaC & Terraform

Git workflows

Education

Bachelor's degree in Computer Science or related

Job description

Responsibilities

• Architect scalable platform solutions: design, build, and govern highly performant, distributed data solutions on GCP, leveraging BigQuery, Dataproc, Dataflow, BigTable, Cloud Storage, Cloud Run, Pub/Sub, and Vertex AI to support global analytical, operational, and real‑time streaming use cases.
• Lead cloud data integration & streaming: design and implement high‑volume, event‑driven streaming pipelines using GCP Pub/Sub, Apache Beam, and Apache Kafka; build real‑time stream processing systems to ingest and land events into optimized BigQuery analytical layers and low‑latency BigTable operational stores.
• Design & productize APIs: architect and implement secure, versioned, and highly performant RESTful and gRPC APIs that expose platform services and data products; define API contracts, versioning strategies, and authentication/authorization patterns (OAuth, mTLS) for reliable self‑service access.
• Enforce schema‑first governance: champion data contract standardizations and shift‑left data quality practices; define and enforce data schemas using Protobuf and centralized schema registries, ensuring robustness, backward compatibility, and semantic alignment across distributed producer and consumer teams.
• Build reusable platform frameworks: design opinionated, extensible frameworks and libraries that abstract underlying infrastructure complexity; enable product engineering teams to bootstrap and scaffold production‑ready data pipelines and microservices through standardized templates, configuration‑driven workflows, and automated code generation with Python and Jinja templating.
• Drive platform and data strategy: collaborate with business, product, and engineering leaders to translate organizational objectives into executable architectural roadmaps; establish clear boundaries between core platform responsibilities and feature‑team domain boundaries.
• Automate infrastructure‑as‑code (IaC): lead the GitOps lifecycle by designing and maintaining Terraform (HCL) configurations and automated CI/CD pipelines (GitHub Actions, Tekton) to ensure consistent, secure, and rapid infrastructure deployments across Dev, QA, and Production environments.
• Model and transform data: design and govern modern data modeling patterns (dimensional modeling, Data Vault, event‑based streaming schemas) across analytical tables, relational databases, and flat‑file stores; orchestrate distributed Spark batch processing, complex SQL transformations, and ML training pipelines using Apache Airflow.
• Operate containerized services: build, publish, and secure container images using Buildah and Docker; manage deployments to Cloud Run and Google Kubernetes Engine (GKE); automate container image promotion, environment progression, and artifact registry security through dedicated CI/CD pipelines.
• Provide technical leadership & enablement: provide hands‑on mentorship and architectural guidance to data engineering teams; establish best practices, design patterns, and style guides; build developer‑enablement tooling to minimize onboarding friction and accelerate time‑to‑market.

Requirements

• Education: Bachelor’s degree in Computer Science, Data Engineering, Information Technology, or a related technical field (or equivalent practical experience).
• Experience: 7+ years of progressive technical experience in data architecture, platform engineering, or enterprise‑scale data engineering.
• Cloud architecture: 3+ years of experience designing and operating end‑to‑end data platforms on Google Cloud Platform (GCP), AWS, or Azure.
• Programming proficiency: 3+ years of practical application development experience using Python, SQL, and Java (or Go/Bash) for high‑performance pipeline development, API creation, and automation scripting.
• Platform scaffolding: experience building at least one centralized developer ecosystem or developer‑facing templating framework using Git submodules or standardized repositories.
• Location: Must live within reasonable commuting distance of Dearborn, MI to support a hybrid work model.