Senior Data Engineer
Remote (Poland or Romania) | Monthly travel to Geneva | Full-time | Employment via Employer of Record
DLG builds LuxuryIQ, a market intelligence platform used by 80+ luxury brands. The platform ingests data from 50+ sources — social platforms, search engines, secondary markets, advertising networks — through BigQuery into client-facing analytics and AI-powered tools.
The data infrastructure exists and runs in production. It needs an experienced engineer to take ownership, improve reliability, expand coverage, and enforce quality standards across the full pipeline.
What makes this different: your clean, documented data will power a conversational AI layer (via Model Context Protocol) that lets luxury brand CEOs query market intelligence in natural language. You’re not building another analytics pipeline — you’re building the foundation of an AI product.
SCOPE ●
As our most senior individual contributor on the data team, you will set the technical direction for pipeline architecture and data quality standards. You will take end-to-end ownership of mission-critical pipelines — from raw ingestion through to clean, documented delivery in BigQuery — and be the go-to technical authority on data behaviour across the platform.
Responsibilities
- Design and develop data processing, cleansing, transformation, and QA scripts using Python and SQL
- Own the operation, maintenance, and enhancement of existing mission-critical data pipelines and ETLs
- Monitor pipeline reliability and data quality standards; identify and close coverage gaps systematically
- Optimise ETL performance and migrate legacy pipelines into a more scalable architecture
- Define and enforce data quality standards across the full pipeline — you treat data quality as non-negotiable
- Contribute to architectural decisions on ingestion, transformation, and delivery layer design
- Own the full data lifecycle documentation: every transformation rule, edge case, and business logic decision must be spec'd, not held in someone's memory
- Fetch datasets from external data sources: REST APIs, JSON, CSV
- Perform data migrations and batch data updates
- Conduct exploratory analysis to support new business requirements and data source onboarding
- Develop proof-of-concept pipelines for new data initiatives
REQUIREMENTS
Technical
- Graduate in Computer Science, Information Systems, Statistics, Mathematics, or related field
- 7+ years of relevant experience in data engineering, database or ETL development
- Excellent Python skills (pandas, numpy); able to write production-grade pipeline code
- Excellent SQL skills and strong database design and development experience
- Proven ETL development and long-term maintenance experience
- Apache Airflow experience
- Entity resolution and deduplication across heterogeneous, multi-source datasets
- Web scraping, crawler development, and API integration experience
- Working knowledge of AI/LLM tooling (Claude Code, Copilot, or equivalent)
Preferred
- BigQuery (primary stack) and/or GCP experience — strongly preferred
- PostgreSQL experience
- dbt experience
- Experience with AliCloud, AWS, or similar cloud platforms
- Big Data processing technologies: Databricks, Spark, Redis, Kafka
Behavioural
- You take full ownership of your scope and drive it independently
- You identify gaps in pipeline coverage and close them systematically
- You document thoroughly — undocumented systems are unreliable systems
- You treat data quality as non-negotiable — you've been burned by bad data before and it shows
IDEAL BACKGROUND ●
You’ve worked with scraped, unstructured, messy real-world data at scale. You know what it’s like to reconcile the same entity across dozens of sources with different naming conventions, missing fields, and inconsistent formats.
Strong-fit industries: Price comparison and travel aggregation platforms, marketplace intelligence and web analytics, real estate platforms, alternative data providers for finance, or competitive intelligence companies.
The key differentiator: Have you built a data product that external customers pay for? That’s a different mindset than running internal data ops. Interest in the luxury industry is a plus but not required — the data engineering challenges are universal.
