Data Engineer - Samba Platform

WarsawInternational Full Time Employeeentry

DevOps & InfrastructureData EngineeringData EngineerDataData & AI

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Overview

Samba is an AI-powered media intelligence company on a mission to give marketers the complete picture of their audiences. Our AI indexes media consumption across millions of smart TVs and 2.

Technical Tools

DevOps & InfrastructureData EngineeringData EngineerDataData & AI

Samba is an AI-powered media intelligence company on a mission to give marketers the complete picture of their audiences. Our AI indexes media consumption across millions of smart TVs and 2.5 billion web pages, combining that data with third-party signals through the Samba Knowledge Graph, a map of the real interests, behaviors, and purchase intent of 1.5 billion user profiles globally. Brands, agencies, publishers, and platforms use Samba to make smarter decisions across every stage of the marketing funnel.

We are seeking a skilled Data Engineer to strengthen our data platform team. Our team is tasked with building and maintaining the data platform that powers the entire organization; from ingestion to analytics and reporting; from valuable viewership and contextual datasets to scalable applications that enable data-driven decision making. While our organization is hybrid, you'll be building mostly on AWS, Databricks, BigQuery and Snowflake technology. The ideal candidate will have strong experience in cloud-based data engineering, distributed data processing, and data governance and metadata management to support analytics, reporting, and machine learning use cases.

Build scalable data product architecture, capable of supporting both internal and external data consumers

Responsible for modernizing our data frameworks and integrations with Databricks and BigQuery

Upgrade and reduce toil for developers on Apache Airflow

Develop and optimize data transformations using Apache Spark (PySpark/Scala)

Build procedures and guidelines to help teams operate with data

Identify bottlenecks in our development lifecycle and find solutions to improve them

Drive innovation throughout the tech org by evangelizing and educating teams on best practices and new technologies

Work directly with our data teams and FinOps teams to drive efforts that span across teams

Implement data governance, access control, and auditing using Databricks Unity Catalog

Build and integrate automated, reusable data validation suites using data quality frameworks (Great Expectations or similar)Implement monitoring and anomaly detection systems for data quality, reliability and performance

Develop and manage REST APIs to support secure data access, automation, and integration

Collaborate with data scientists, analysts, and software engineers to deliver governed, reusable data assets

Implement monitoring, logging, and alerting for data workflows

Optimize cost and performance of cloud-based data infrastructure

Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience)

5+ years of experience in data engineering or a related role

Strong hands-on experience with Databricks, Apache Spark and BigQuery or Snowflake

Proven experience with Modern table formats such as Delta Lake and Iceberg

A deep understanding of the data lifecycle and how teams operate with data

Hands-on experience implementing data governance and metadata management using Databricks Unity Catalog

Experience managing and extending Apache Airflow (custom operators, plugins, infrastructure)

Experience with Kubernetes

Solid experience with AWS cloud services, especially S3 and data-related services

Experience with data validation and data quality principles and working with SLA systems

Proficiency in Python and SQLExperience with data modeling, data lakes, and lakehouse architectures and a strong understanding of distributed systems and big data processing

Preferred Qualifications

Experience with Infrastructure as Code (Terraform)

Knowledge of CI/CD pipelines for data platforms

Experience working in Agile/Scrum environments

AWS, Databricks, or cloud data certifications

Experience working with data clean room environments, including implementation or collaboration within Snowflake, Databricks, or BigQuery ecosystems

Experience with streaming data pipelines (Kafka, Flink)

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