Data Platform Engineer

Iron Mountain•Massachusetts - Remote, MA

3d•$93,400 - $124,500

About The Position

At Iron Mountain, we are seeking a Data Platform Engineer to be a key execution engine for our core data DNA. This role reports directly to the Senior Director of Technology Enablement and Acceleration, bridging the gap between high-level data architecture and robust, secure technical execution. 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. Additionally, you will serve as a guardian of data security, ensuring our API backbone and data lake are highly optimized, performant, and tightly governed. Iron Mountain provides expert, sustainable solutions in records and information management, digital transformation services, data centers, asset lifecycle management, and fine art storage, handling, and logistics, partnering with 225,000 customers worldwide.

Requirements

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

Nice To Haves

Hands-on exposure to or proficiency in Google BigQuery (specifically optimizing Capacitor storage and Dremel execution).
Hands-on exposure to or proficiency in Astronomer / Apache Airflow and dbt (Data Build Tool).
Hands-on exposure to or proficiency in MuleSoft Anypoint Platform & API Gateways.
Hands-on exposure to or proficiency in Enterprise search tools, Vector databases, or Google Cloud GenAI/Vertex AI tools.
Hands-on exposure to or proficiency in Google Dataplex, IAM, and policy-based access control.

Responsibilities

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).
Implement and optimize search-centric data pipelines to support enterprise search functionality, ensuring high query performance and relevant data retrieval across the Foundry ecosystem.
Execute data transformations across the Ingestion, Refined, and Reporting layers of the BigQuery data lake, keeping data organized for search efficiency.
Proactively identify bottlenecks in Astronomer/Airflow DAGs and BigQuery SQL transformations. Optimize partitioning, clustering, and slot utilization to control compute costs and meet SLAs.
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.
Act as a systems thinker by ensuring that pipeline modifications do not negatively impact downstream BigQuery tables or upstream API schemas.
Apply strong data security principles by configuring and managing Row-Level Security (RLS) and Column-Level Security (CLS) within BigQuery.
Work closely with Data Stewards to implement data masking, tokenization, and governance policies (GDPR/CCPA) utilizing Google Dataplex and IAM policy tags.
Ensure all data pipelines and API exposures conform strictly to enterprise security baselines and robust authentication protocols.
Partner closely with the AI Platform Engineering team to supply highly curated, clean, and optimized datasets for machine learning models and LLM applications.
Assist in building and maintaining the pipeline architecture required to feed AI feature stores or vector databases used in cognitive search applications.