Senior Data Engineer at Provectus

Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses. We are seeking a talented and experienced Data Engineer to join our team at Provectus. As part of our diverse practices, including Data, Machine Learning, DevOps, Application Development, and QA, you will collaborate with a multidisciplinary team of data engineers, machine learning engineers, and application developers. You will encounter numerous technical challenges and will have the opportunity to contribute to the internal solutions, engage in R&D activities, providing an excellent environment for professional growth. Responsibilities: • 5+ years of experience in data engineering; • Experience in AWS; • Experience handling real-time and batch data flow and data warehousing with tools and technologies like Airflow, Dagster, Kafka, Apache Druid, Spark, dbt, etc.; • Proficiency in programming languages relevant to data engineering, such as Python and SQL; • Proficiency with Infrastructure as Code (IaC) technologies like Terraform or AWS CloudFormation; • Experience in building scalable APIs; • Familiarity with Data Governance aspects like Quality, Discovery, Lineage, Security, Business Glossary, Modeling, Master Data, and Cost Optimization; • Upper-Intermediate or higher English skills; • Ability to take ownership, solve problems proactively, and collaborate effectively in dynamic settings. Nice to Have: • Experience with Cloud Data Platforms (e.g., Snowflake, Databricks); • Experience in building Generative AI Applications (e.g., chatbots, RAG systems); • Relevant AWS, GCP, Azure, Databricks certifications; • Knowledge of BI Tools (Power BI, QuickSight, Looker, Tableau, etc.); • Experience in building Data Solutions in a Data Mesh architecture. Responsibilities: • Collaborate closely with clients to deeply understand their existing IT environments, applications, business requirements, and digital transformation goals; • Collect and manage large volumes of varied data sets; • Work directly with ML Engineers to create robust and resilient data pipelines that feed Data Products; • Define data models that integrate disparate data across the organization; • Design, implement, and maintain ETL/ELT data pipelines; • Perform data transformations using tools such as Spark, Trino, and AWS Athena to handle large volumes of data efficiently; • Develop, continuously test, and deploy Data API Products with Python and frameworks like Flask or FastAPI. What you'll get: • Long-term B2B collaboration; • Paid vacations and sick leaves; • Public holidays; • Compensation for medical insurance or sports coverage; • External and Internal educational opportunities and AWS certifications; • A collaborative local team and international project exposure.