Data Platform Engineer

Data Platform Engineer

At Iron Mountain we know that work, when done well, makes a positive impact for our customers, our employees, and our planet. That's why we need smart, committed people to join us. Whether you're looking to start your career or make a change, talk to us and see how you can elevate the power of your work at Iron Mountain.

We provide expert, sustainable solutions in records and information management, digital transformation services, data centers, asset lifecycle management, and fine art storage, handling, and logistics. We proudly partner every day with our 225,000 customers around the world to preserve their invaluable artifacts, extract more from their inventory, and protect their data privacy in innovative and socially responsible ways.

Are you curious about being part of our growth story while evolving your skills in a culture that will welcome your unique contributions? If so, let's start the conversation.

As a Data Platform Engineer, you will be a key execution engine for Iron Mountain's core data DNA. Reporting directly to the Senior Director of Technology Enablement and Acceleration, you will bridge the gap between high-level data architecture and robust, secure technical execution.

In this role, you will actively build and optimize our Data Foundry platform, implement advanced enterprise search capabilities across structured and unstructured datasets, and partner closely with the AI Platform Engineering team to power downstream intelligent workflows. You will also serve as a guardian of data security, ensuring that our API backbone and data lake are highly optimized, performant, and tightly governed.

Core Responsibilities

Data Foundry & Enterprise Search Execution

• Structured & Unstructured Ingestion: Build and maintain scalable pipelines within the Data Foundry project to ingest, process, and index both structured transactional data and unstructured enterprise data (e.g., documents, media).
• Enterprise Search Indexing: Implement and optimize search-centric data pipelines to support enterprise search functionality, ensuring high query performance and relevant data retrieval across the Foundry ecosystem.
• Multi-Tier Lifecycle Management: Execute data transformations across the Ingestion, Refined, and Reporting layers of the BigQuery data lake, keeping data organized for search efficiency.

Architecture Optimization & API Backbone Support

• Query & Pipeline Tuning: Proactively identify bottlenecks in Astronomer/Airflow DAGs and BigQuery SQL transformations. Optimize partitioning, clustering, and slot utilization to control compute costs and meet SLAs.
• Secure API Connectivity: Collaborate on the development and maintenance of the API backbone strategy, ensuring data is securely and efficiently syndicated between legacy applications, SaaS systems, and the Data Foundry.
• Systems Synchronization: Act as a systems thinker by ensuring that pipeline modifications do not negatively impact downstream BigQuery tables or upstream API schemas.

Data Access Governance & Security

• Granular Security Implementation: Apply strong data security principles by configuring and managing Row-Level Security (RLS) and Column-Level Security (CLS) within BigQuery.
• Compliance Enforcer: Work closely with Data Stewards to implement data masking, tokenization, and governance policies (GDPR/CCPA) utilizing Google Dataplex and IAM policy tags.
• Secure Architecture Maintenance: Ensure all data pipelines and API exposures conform strictly to enterprise security baselines and robust authentication protocols.

AI Platform Partnership

• AI Data Readiness: Partner closely with the AI Platform Engineering team to supply highly curated, clean, and optimized datasets for machine learning models and LLM applications.
• Feature Store & Vector Integration: Assist in building and maintaining the pipeline architecture required to feed AI feature stores or vector databases used in cognitive search applications.

Technical Ecosystem

• Data Warehouse: Google BigQuery (specifically optimizing Capacitor storage and Dremel execution).
• Orchestration & Transformation: Astronomer / Apache Airflow and dbt (Data Build Tool).
• Integration Tier: MuleSoft Anypoint Platform & API Gateways.
• Search & AI Frameworks: Enterprise search tools, Vector databases, or Google Cloud GenAI/Vertex AI tools.
• Governance: Google Dataplex, IAM, and policy-based access control.

Qualifications & Experience

• Experience: 3–5 years of professional software or data engineering experience in an enterprise cloud environment (GCP preferred).
• SQL & Programming Mastery: Advanced SQL skills (analytical functions, query optimization) and proficiency in Python, PySpark, and/or Scala.
• Security Mindset: Demonstrable experience implementing data access controls, encryption-at-rest/in-transit, and managing secure API endpoints.
• Collaborative Mindset: Proven ability to work cross-functionally with technical peers in AI, Infrastructure, Security, and Product teams.

Reasonably expected salary range: $93,400.00 - $124,500.00

Please note that an employee's starting salary may vary based on a variety of factors. Where State, Municipal, Provincial, Territorial or other legal minimum wages exceed the federal minimum wage, employees are entitled to the higher rate.

Category: Technology