About Apertera
Apertera is leading the evolution of language solutions for high-stakes content. We partner with enterprises as an extension of their teams, combining professional expertise with Adaptive AI technology that is continuously refined by client context.
For more than twenty years, Apertera has set the bar for legal, financial, and regulatory translation, serving the most rigorous buyers, including over 75% of major national Canadian law firms, all major banks, and leading securities regulators.
Apertera is Canadian-owned, ISO 17100 and SOC 2 certified.
Our core values:
Innovation
Dedication
Fanatical commitment to quality and service
Resourcefulness
Collaboration
Role Overview
As the Data Engineering Team Lead, you will be the technical lead and people manager for a specialized team at the intersection of Big Data, Global Communications, and Generative AI. You will oversee the development of our enterprise Data Warehouse, ensuring that our language assets (TMs, Glossaries, and Metadata) are structured, searchable, and optimized for human translators, machine translation searches and machine learning models.
Beyond traditional data engineering, you will collaborate with multiple teams on the design of the platform interface and the indexing strategies that power our next-generation localization workflows. Your unique value lies in bridging the gap between high-level data architecture, the nuanced translation domain, and the emerging requirements of Retrieval-Augmented Generation (RAG).
Key Responsibilities
1. Data Strategy & Warehousing
Architecture: Define the roadmap for our data warehouse, ensuring high availability and performance for massive multilingual datasets.
Data Cataloging & Governance: Implement robust cataloging solutions to ensure data lineage and "discoverability" across the organization.
Interface Development: Lead the creation of a user-centric interface that allows stakeholders to interact with, query, and extract data from the platform.
2. Translation & Localization Domain
Linguistic Asset Management: Manage the lifecycle of Translation Memories (TMs) and Terminology Databases.
Systems Expertise: Optimize integrations between our data platform and CAT tools and TMS systems (e.g., Phrase, Trados, MemoQ).
Domain Integration: Ensure data pipelines respect the nuances of translation metadata, XLIFF structures, and regional variants.
3. ML & GenAI Integration
RAG & Indexation: Oversee the creation and maintenance of Vector Databases and semantic search indexes to support Retrieval-Augmented Generation for automated translation and content creation.
Data Preparation for LLMs: Architect pipelines that clean, chunk, and format localization data for fine-tuning or prompting Large Language Models (LLMs).
Quality & Evaluation: Support the implementation of automated quality estimation (QE) and LLM-based evaluation metrics for translated content.
4. Leadership & Mentorship
Team Management: Lead a cross functional team of Linguists, Software Developers, Devops and Localization Engineers, providing technical guidance and mentorship.
Cross-functional Collaboration: Act as the liaison between Data, Localization, and AI/ML Research teams.
Required Qualifications
Experience: 5+ years in Data Engineering, with at least 2 years in a leadership or senior capacity.
Technical Stack: Proficiency in SQL, Python, ETL Pipelines, and cloud data platforms (e.g., AWS S3 Data Lakes, AWS Athena, AWS Redshift, AWS Glue).
AI/ML Fundamentals: Solid understanding of the GenAI lifecycle, specifically regarding how data is indexed for RAG (e.g., Pinecone, Milvus, or Qdrant).
Domain Knowledge: Understanding of the localization industry, including experience with TMX, TBX, and CAT tool workflows.
Product Mindset: Experience building and deploying production ready internal tools or interfaces (e.g., Streamlit, React) to democratize data access.
Preferred Skills
Familiarity with embedding models and semantic similarity scoring.
Knowledge…