DATA ENGINEER

Direct employment by the client – company employee from day one! Hybrid work model

we are looking for a skilled SENIOR DATA ENGINEER with strong expertise in AWS and Data Lake technologies to join the data engineering team at a leading AI company. If you have a proven background in AWS and building scalable Data Lakes, this is your opportunity to take on a pivotal role in shaping the company's data infrastructure!

As a Senior Data Engineer, you will become the company’s go-to expert in managing and optimizing the AWS-based Data Lake. You’ll be responsible for designing, implementing, and maintaining robust ETL/ELT pipelines on AWS to support seamless data flows and empower data-driven decision-making across the organization.

Key Responsibilities

Architect, develop, and optimize advanced data pipelines (ETL/ELT) using AWS services to ingest data from diverse sources into a highly scalable Data Lake.

Ensure high-quality, secure, and performant data storage and processing within the AWS environment.

Lead end-to-end data pipeline development, from design through deployment, with a strong focus on AWS cloud architecture.

Collaborate with stakeholders to understand business needs and translate them into efficient, AWS-native data solutions.

Bachelor’s degree in a technical field or equivalent practical experience.

5+ years of experience in a Data Engineering role, with a focus on cloud-based data solutions.

Strong expertise in AWS platform services, including S3, Redshift, Lambda, and Glue.

Extensive experience designing, building, and managing Data Lake architectures on AWS.

Proficiency in Python for data pipeline development, and SQL for querying and data manipulation.

Proven experience with data modeling and designing efficient ETL/ELT processes for large-scale data ingestion and transformation.

Familiarity with data quality practices, including validation, testing, and monitoring in an AWS environment.

Experience with Git, CI/CD workflows, and Docker.

Hands-on experience with big data frameworks, such as Spark, in the context of AWS.

Preferred Qualifications

Experience working with Data Science teams (MLOps) to support machine learning pipelines on AWS.

Knowledge of BI tools such as Tableau, Looker, or Power BI for data visualization.