Overview
Job Opening: Maps Data Engineer
Location: Hyderabad
Experience: 6+ years
About Antal:
Antal International, East Patel Nagar Delhi, is a leading recruitment consultancy having expertise in connecting top talent across IT, Manufacturing and FMCG industries with leading organizations.
About the role:
We are looking for a Maps Data Engineer to support the development of machine learning systems that power mapping and geospatial intelligence. This role focuses on building scalable data pipelines, processing large-scale geospatial datasets, and preparing data for machine learning models used in mapping and location-based services.
The engineer will work closely with Data Science and ML Engineering teams to build and maintain data workflows that support ML model training, evaluation, and deployment for map-related applications.
Key Responsibilities
- Build and maintain scalable data pipelines for processing large-scale maps and geospatial datasets.
- Develop ETL workflows and large-scale data processing pipelines using Spark (Scala or PySpark).
- Process and prepare GPS trace data, map datasets, and geospatial information for machine learning models.
- Design data benchmarks and performance monitoring frameworks for map data processing pipelines.
- Support ML pipeline operationalization, including batch and streaming ingestion of map data.
- Develop backend components and services supporting map data processing and ML workflows.
- Collaborate with Data Scientists and ML Engineers to understand data requirements for model training and inference.
- Apply machine learning and data science techniques to extract insights from location and mobility datasets.
- Take ownership of delivering data features, pipelines, and improvements on time.
Required Skills
- Strong programming experience in Python.
- Hands-on experience with PySpark or Scala with Apache Spark for large-scale data processing.
- Good understanding of data engineering concepts and distributed data processing.
- Experience working with machine learning workflows and data preparation for ML models.
- Experience with backend development and data-driven services.
- Strong understanding of data science and machine learning concepts, including:
- Classification
- Clustering
- Feature engineering
- Anomaly detection
- Knowledge of classical ML algorithms such as SVM, Random Forest, Naive Bayes, and KNN.
- Experience with deep learning frameworks such as PyTorch.
- Proficiency in data science libraries including Scikit-learn, NumPy, Pandas, and Polars.
- Strong analytical and problem-solving skills.
Nice to Have
- Experience working with GPS trace data or geospatial datasets.
- Exposure to maps, mobility data, or location intelligence platforms.
- Experience with LLMs, transformers, or open-source models from HuggingFace.
- Experience in fine-tuning LLMs or working with multimodal models (VLMs).
- Experience with textual and image data preprocessing.
- Familiarity with prompt engineering techniques.