Must have

• Python

• Java

• SQL

• Jenkins

• CloudFormation

• Terraform

• Git

• Docker

• Spark

• PySpark

• English

Nice to have

• Redshift

Requirements description

• 5–8 years of relevant experience.
• Knowledge of DSCS and DPTM is a plus.
• Strong experience with AWS services: S3, IAM, Redshift, Sagemaker, Glue, Lambda, Step Functions, CloudWatch.
• Hands-on experience with platforms like Databricks and Dataiku.
• Proficiency in Python or Java, SQL (preferably Redshift), Jenkins, CloudFormation, Terraform, Git, Docker.
• 2–3 years of experience with Spark / PySpark.
• Strong communication skills and ability to document technical processes.

Offer description

Original text.  Show translation

Start Date: ASAP / within 1 month / flexible

Work Model: Fully remote, B2B contract via Experis (150-170) PLN/h + vat)

Job Description: We are looking for an experienced Senior Data Engineer to join our team and take ownership of designing, developing, and maintaining advanced data pipelines. You will collaborate closely with Product Analysts, Data Scientists, and Machine Learning Engineers to deliver clean, structured, and business-ready data.

Our Offer:

• Medicover healthcare package
• Multisport card
• Access to an e-learning platform
• Group life insurance

Opis Firmy

Experis to światowy lider rekrutacji specjalistów i kadry zarządzającej w kluczowych obszarach IT. Z nami znajdziesz konkurencyjne oferty zatrudnienia oraz ciekawe projekty IT skierowane zarówno do ekspertów z wieloletnim doświadczeniem, jak i osób, które dopiero zaczynają swoją przygodę w branży IT.

Oferujemy rekrutacje menedżerów i wysoko wykwalifikowanych konsultantów z doświadczeniem w branży IT. Experis jest częścią ManpowerGroup i został uznany za jedną z najbardziej etycznych firm na świecie.

Your responsibilities

1. Design, develop, and maintain ETL/ELT pipelines to extract data from various sources and load it into data lakes and data warehouses.
2. Define data transformation rules and build data models.
3. Collaborate with analytics and ML teams to identify and transform data for better usability.
4. Implement data quality checks, maintain data catalogues, and use orchestration, logging, and monitoring tools.
5. Apply test-driven development methodology in pipeline creation.
6. Document processes in line with SDLC standards.