Md Robiuddin
Data Engineer with 4+ years building production data pipelines, retrieval-augmented AI systems and large-scale geospatial data products. M.Sc. researcher in time-series forecasting at Trent University, Peterborough, Canada.
Experience
Data Engineer
Nov 2025 – Oct 2026Hjemla AS, Norway- Built a real-time RAG pipeline (FastAPI) that turns property sales PDFs into chunked text and embeddings for semantic retrieval and structured AI outputs, with a database-backed cache that cut repeat-request latency.
- Engineered a PostGIS + dbt geospatial pipeline computing distances between 4M+ properties and risk and noise zones, reducing processing time from months to days.
Data Engineer
Aug 2021 – Oct 2025Softwrd Ltd, Dhaka (data engineering team of Placepoint AS, Norway)- Led a team of 3 delivering modular, Airflow-orchestrated ELT pipelines deployed with Docker and AWS Lambda.
- Built reusable data models in SQL, Python, dbt and PySpark for analytics, marketing and ML use cases; refactored legacy pipelines for a 40% performance gain.
- Introduced data validation and monitoring, CI/CD, code review and testing practices across the team.
Graduate Teaching Assistant
Jan 2025 – presentTrent University · Software Design & Modelling, Artificial IntelligencePython Programming Instructor
Feb 2024 – Dec 2024Center for Development of IT Professionals · mentored 100+ studentsSelected projects
LLM-powered document analyzer
Real-time RAG over unstructured sales documents: PDF parsing, chunking, embeddings, ranked retrieval and result caching.
Geospatial risk proximity
Closest-distance computation for 4M+ properties against risk and noise zones with optimized spatial joins and incremental models.
Real-estate & automotive ELT suite
Modular pipelines for daily listings, new-build trackers and company pages feeding dashboards and data products.
softwrdwrangler
Open-source Python library on PyPI: URI-based S3 read/write for CSV, JSON, Pickle and GeoJSON in cloud-native pipelines.
Research & publications
Target-Queried Channel Attention: Measuring and Exploiting Cross-Channel Context in Multivariate Time-Series ForecastingIEEE ICTAI 2026 · DOI 10.1109/ICTAI70596.2026.00255
M.Sc. research: time-series forecastingCross-channel learning and state-space models (Mamba) for multivariate forecasting · Trent University
Skills
PythonSQLApache AirflowdbtPySparkPostgreSQL / PostGISMongoDBAWS (S3, Lambda)DockerCI/CDFastAPIRAG & embeddingsvLLMPyTorchTime-series forecasting