Upstaff’s Guide to Hire Data and Analytics Team in 2025

- 12+ years experience working in the IT industry; - 12+ years experience in Data Engineering with Oracle Databases, Data Warehouse, Big Data, and Batch/Real time streaming systems; - Good skills working with Microsoft Azure, AWS, and GCP; - Deep abilities working with Big Data/Cloudera/Hadoop, Ecosystem/Data Warehouse, ETL, CI/CD; - Good experience working with Power BI, and Tableau; - 4+ years experience working with Python; - Strong skills with SQL, NoSQL, Spark SQL; - Good abilities working with Snowflake and DBT; - Strong abilities with Apache Kafka, Apache Spark/PySpark, and Apache Airflow; - Upper-Intermediate English.

- Data Engineer with a Ph.D. degree in Measurement methods, Master of industrial automation - 16+ years experience with data-driven projects - Strong background in statistics, machine learning, AI, and predictive modeling of big data sets. - AWS Certified Data Analytics. AWS Certified Cloud Practitioner. Microsoft Azure services. - Experience in ETL operations and data curation - PostgreSQL, SQL, Microsoft SQL, MySQL, Snowflake - Big Data Fundamentals via PySpark, Google Cloud, AWS. - Python, Scala, C#, C++ - Skills and knowledge to design and build analytics reports, from data preparation to visualization in BI systems.

- 10+ years in Forecasting, Analytics & Math Modelling - 8 years in Business Analytics and Economic Processes Modelling - 5 years in Data Science - 5 years in Financial Forecasting Systems - Master of Statistics and Probability Theory (diploma with honours), PhD (ABD) - BSc in Finance - Strong knowledge of Math & Statistics - Strong knowledge of R, Python, VBA - Strong knowledge of PostgreSQL and MS SQL Server - 3 years in Web Development: Knowledge of C#, .Net and JavaScript for web development - Self-motivated, conscientious, accountable, addicted to data processing, analysis & forecasting - Engineering, Understanding AI and LLMs

Software Engineer with proficiency in data engineering, specializing in backend development and data processing. Accrued expertise in building and maintaining scalable data systems using technologies such as Scala, Akka, SBT, ScalaTest, Elasticsearch, RabbitMQ, Kubernetes, and cloud platforms like AWS and Google Cloud. Holds a solid foundation in computer science with a Master's degree in Software Engineering, ongoing Ph.D. studies, and advanced certifications. Demonstrates strong proficiency in English, underpinned by international experience. Adept at incorporating CI/CD practices, contributing to all stages of the software development lifecycle. Track record of enhancing querying capabilities through native language text processing and executing complex CI/CD pipelines. Distinguished by technical agility, consistently delivering improvements in processing flows and back-end systems.

- 8 years experience with various data disciplines: Data Engineer, Data Quality Engineer, Data Analyst, Data Management, ETL Engineer - Automated Web scraping (Beautiful Soup and Scrapy, CAPTCHAs and User agent management) - Data QA, SQL, Pipelines, ETL - Data Analytics/Engineering with Cloud Service Providers (AWS, GCP) - Extensive experience with Spark and Hadoop, Databricks - 6 years of experience working with MySQL, SQL, and PostgreSQL; - 5 years of experience with Amazon Web Services (AWS), Google Cloud Platform (GCP) including Data Analytics/Engineering services, Kubernetes (K8s) - 5 years of experience with PowerBI - 4 years of experience with Tableau and other visualization tools like Spotfire and Sisense; - 3+ years of experience with AI/ML projects, background with TensorFlow, Scikit-learn and PyTorch; - Extensive hands-on expertise with Reltio MDM, including configuration, workflows, match rules, survivorship rules, troubleshooting, and integration using APIs and connectors (Databricks, Reltio Integration Hub), Data Modeling, Data Integration, Data Analyses, Data Validation, and Data Cleansing) - Upper-intermediate to advanced English, - Henry is comfortable and has proven track record working with North American timezones (4hour+ overlap)

Engineer with 10+ years’ experience in AI, excelling in NLP, GenAI, computer vision, and model deployment. Expertise in Python, SQL, PySpark, and cloud platforms. Established record of enhancing AI-driven services, improving workflows, and boosting efficiency. Proven capabilities in developing robust ML pipelines, integrating state-of-the-art technologies like LLMs and RAG pipelines, and delivering solutions across diverse industries. Advanced in web and full-stack development, data engineering, analysis, and DevOps practices, underpinned by solid formal education in computer science.

* Experienced Data Engineer and BI Developer with 6+ years of expertise in Database Design and Business Intelligence Development. * Proficient in cloud technologies such as Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure. * Skilled in building high-performance data integration and workflow solutions, including ETL operations for data warehousing and supporting OLAP, OLTP, and Data warehouse systems. Experience in optimizing DWH performance and automating data pipelines; * Modern data engineer skills such as data modeling, data warehousing, data lake, data governance, and data quality. * Experience with big data technologies such as Hadoop, Spark, and Kafka, and experience with data streaming and real-time data processing. * Proficiency in SQL and NoSQL databases, Snowflake, and ClickHouse * Data visualization tools such as Tableau or Power BI. * Programming languages such as Python, Java, or Scala, and understanding of machine learning concepts, with experience building and deploying machine learning models. * Experience with CI/CD, data governance, and security best practices.

- Applied data scientist and MLOps engineer with 5+ years in PHY security and ML for wireless systems. - End-to-end ML delivery: data wrangling, feature engineering, model development (scikit-learn, PyTorch), evaluation, and CI-friendly deployment. - Built ML-driven performance measurement and scheduling/optimization services; exposed via REST APIs; productionized on Microsoft Azure (ML Studio, Function Apps). - Strong data engineering foundation: SQL modeling and queries (Azure Data Studio), data pipelines, and reproducible experimentation. - Methods expertise: supervised/unsupervised learning, reinforcement learning, adversarial/robust modeling, optimization techniques. - Practical MLOps: containerized services, API design, monitoring-oriented deployment patterns, version control (Git). - Domain background: physical-layer authentication, anti-jamming/anti-spoofing, and federated/edge learning research. - Track record of translating complex problem statements into scalable, measurable data products with clear product impact.