Join Provectus as a Senior Data Engineer, working on ML infrastructure, data engineering projects, and collaborating with a multidisciplinary team to resolve technical challenges.
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.
Top Skills
Airflow
Apache Druid
AWS
Aws Cloudformation
Dagster
Databricks
Dbt
Fastapi
Flask
Kafka
Looker
Power BI
Python
Quicksight
Snowflake
Spark
SQL
Tableau
Terraform
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