Data Engineer

Türkiye·Ankaramid

Data EngineerData

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Quick Summary

Overview

Who We AreOPLOG is the tech engine behind seamless e‑commerce fulfillment for top brands across Türkiye, Europe, and the US. By fusing in‑house software with robotics and automation, we erase the line between the physical and digital worlds—delivering post‑purchase experiences that turn customers…

Key Responsibilities

Design, maintain and optimize batch and streaming data pipelines using Databricks, Apache Spark, and Fivetran. Build and implement data models, products, and platforms with high quality using Databricks ecosystem and dbt for data transformation.

Requirements Summary

Bachelor/Master's degree in related engineering departments such as Computer Engineering, Software Engineering, or Data Science.

Technical Tools

airflowawsdbtdockerkubernetespythonsparksqldata-visualizationdatabase-designetlmachine-learningstreaming-data

Who We Are

OPLOG is the tech engine behind seamless e‑commerce fulfillment for top brands across Türkiye, Europe, and the US. By fusing in‑house software with robotics and automation, we erase the line between the physical and digital worlds—delivering post‑purchase experiences that turn customers into fans and giving our clients an unfair competitive edge.

We’re building a world-class robotic fulfillment ecosystem, and we’re looking for curious minds to shape the future with us.

About the Role
We are looking for a highly experienced, talented, and self-starter Senior Data Engineer who will be responsible for designing and implementing scalable and robust data platforms and solutions using cutting-edge technologies.

What You'll Do

Design, maintain and optimize batch and streaming data pipelines using Databricks, Apache Spark, and Fivetran.
Build and implement data models, products, and platforms with high quality using Databricks ecosystem and dbt for data transformation.
Develop MLOps pipelines and AI-driven solutions to enhance our fulfillment operations and predictive analytics.
Work with product management and product teams to build data-driven products, extract, interpret and present insights using Qlik.
Contribute to the development of the analytical data warehouse and related big data ecosystem on AWS platform.
Implement real-time data processing and streaming architectures using Databricks structured streaming.
Build and maintain dbt models for data transformation and analytics engineering.
Provide support to data analysts and data scientists for their data engineering and ML infrastructure requirements.
End-to-end experience of software development lifecycle with focus on MLOps best practices.

Who You Are

Bachelor/Master's degree in related engineering departments such as Computer Engineering, Software Engineering, or Data Science.
6+ years of professional experience in data engineering with a proven track record of delivering complex data solutions in a production environment.
High proficiency in Python for data engineering, AI/ML development, and SQL.
Experience with dbt (Data Build Tool) for data transformation and analytics engineering.
Deep experience in AWS cloud services (S3, EMR, Glue, Lambda, EC2).
Extensive hands-on experience with Databricks platform and Apache Spark for large-scale data processing.
Experience with Fivetran or similar ELT tools for data integration and pipeline automation.
Proven experience building MLOps pipelines and deploying machine learning models in production using Databricks MLflow.
Experience with data processing frameworks and tools such as RDBMS, NoSQL, and High Scale Databases.
Proven experience building data pipelines using Databricks, Fivetran, and related modern data stack tools.
Experience in real-time and streaming architectures using Databricks Structured Streaming and related technologies.
Strong knowledge of Data Warehouse concepts and modern data lake architectures on AWS.

Nice-to-haves

Familiarity with Qlik Sense/QlikView for business intelligence and data visualization.
Advanced experience with dbt for complex data transformations and data modeling.
Experience with Databricks Delta Lake for data lake management and ACID transactions.
Experience with Databricks MLflow for machine learning lifecycle management.
Knowledge of Apache Airflow or Databricks Workflows for orchestration.
Experience with AWS data services and infrastructure.
Experience with data governance and data quality frameworks within the Databricks ecosystem.
Knowledge of containerization technologies (Docker, Kubernetes) for ML deployment.
Experience with Databricks Unity Catalog for data governance and security.