Prudent Technologies and Consulting, Inc.
Website:
prudentconsulting.com
Job details:
Job summary
We are looking for a Data Specialist with 6–10 years of experience who brings a strong blend of data engineering, data science, and AI capabilities. The role requires hands-on expertise in building scalable data pipelines, preparing and modelling data, developing machine learning solutions, and enabling business teams with reliable, high-quality data products.
The ideal candidate should be comfortable working across the full data lifecycle, from ingestion and transformation to analysis, experimentation, deployment, and monitoring. Strong programming in Python and SQL, experience with cloud data platforms, and practical exposure to AI or GenAI use cases are important for success in this role.
Responsibilities
- Design, build, and maintain scalable batch and real-time data pipelines for structured and unstructured data.
- Develop and optimize ETL/ELT workflows to support analytics, reporting, data science, and AI use cases.
- Build and manage datasets, data models, and data architectures including data lakes and data warehouses.
- Work with large-scale data technologies and distributed processing frameworks such as Spark or similar platforms.
- Partner with business, product, analytics, and engineering teams to translate requirements into data solutions.
- Perform exploratory data analysis, feature engineering, statistical analysis, and predictive modelling.
- Develop, validate, and deploy machine learning or AI models for business use cases.
- Support end-to-end model lifecycle activities including problem framing, experimentation, evaluation, deployment, and production monitoring.
- Ensure data quality, governance, performance tuning, reliability, and observability across data platforms.
- Contribute to AI and GenAI initiatives by preparing high-quality datasets, integrating models, and supporting production-grade AI workflows.
Required skills
- 6–10 years of overall experience in data engineering, data science, or closely related roles.
- Strong hands-on expertise in Python and SQL.
- Experience with ETL/ELT pipelines and modern data integration tools.
- Strong knowledge of relational databases, data modelling, and data warehousing concepts.
- Experience with cloud platforms such as Azure, AWS, or Google Cloud, with preference for cloud data services.
- Exposure to big data or distributed processing tools such as Spark, Kafka, Hadoop, or similar technologies.
- Solid understanding of machine learning techniques, statistical modelling, and data analysis.
- Experience with libraries and tools such as pandas, NumPy, scikit-learn, or equivalent ML/data science frameworks.
- Good understanding of model deployment, validation, and monitoring in production environments.
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