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.
Requirements
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.
Benefits
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
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
data engineeringETLELTdata modelingdata transformationAPI developmentPythonSQLInfrastructure as Codedata warehousing