Senior Software Data Engineer - Founding

6 days ago

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TubeScience

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TubeScience is a leading producer of conversion-optimized video advertisements, focusing on a pay-for-performance model. The company creates a large volume of user-centric video content designed to outperform existing in-house advertising efforts, thereby only charging clients when their videos succeed. TubeScience operates expansive production facilities, equipped with advanced technologies and a diverse team of professionals, all in-house. Working with sophisticated advertisers across various industries, the company emphasizes data-driven creative processes that integrate seamlessly with client brands. TubeScience specializes in video advertising across multiple platforms and is particularly effective for clients capable of high ad spends. Their significant impact on scaling business growth is recognized by major brands in different sectors, and they avoid products that are detrimental to consumer health or based on unsubstantiated claims.

51 - 200 employees

📱 Media

🤝 B2B

📋 Description

• Are you a data enthusiast with a knack for building scalable and reliable systems? TubeScience, one of the world’s largest performance video agencies, is on the hunt for a founding Senior Software Data Engineer to join our team! In this role, you’ll play a key part in developing and maintaining robust data ingestion pipelines, and building an efficient and reliable company data infrastructure. You'll work on projects that bring ad performance data to life, integrating custom logic into cutting-edge tools and optimizing the pipeline architecture for steady, long-term success. • Build & Maintain Ingestion Pipelines: Design and implement highly maintainable and scalable data pipelines, primarily focused on ingesting ad performance data from multiple sources. • API Interactions: Set up and manage API interactions with various data sources to facilitate smooth, efficient, and reliable data pulls. • Infrastructure Setup: Establish the infrastructure necessary for automated, scheduled data pulls, including observability tools and custom settings for complex ingestion tasks. • Custom Logic Integration: Integrate custom ingestion logic into robust systems such as Airbyte, Dagster, Temporal, etc., to ensure seamless data flows. • Pipeline Architecture Improvements: Regularly review our current data ingestion pipeline architecture, identifying areas for improvement and proposing optimization solutions that elevate our performance and scalability. • Collaborate & Lead: Work closely with cross-functional teams to understand data needs, and mentor junior engineers in building robust data pipelines.

🎯 Requirements

• Proven Experience in data engineering with a focus on building, scaling, and maintaining data pipelines and architecture. • Proficiency in Python: Building robust ingestion systems, handling API calls, and creating custom logic for data management. • Advanced SQL Knowledge: Experience with complex queries, optimization, and managing large datasets. • Strong understanding of data ingestion/integration tools such as Airbyte, Dagster, Temporal or similar. • Excellent Problem Solving & Architecture Skills: Ability to analyze and optimize ingestion pipelines for better performance, scalability, and observability. • ETL Expertise: Strong experience designing and implementing ETL (Extract, Transform, Load) processes to efficiently move, clean, and process data from source systems to data warehouses or data lakes. • Solid Communication Skills: Full English proficiency, comfortable collaborating with cross-functional, distributed teams and guiding junior engineers.

🏖️ Benefits

• Impact: This is our founding (first) Senior Data Engineer, who will get the chance to drive and lead product, process, and team growth. You’ll be at the forefront of improving our data infrastructure, directly influencing how we collect, manage, and utilize ad performance data. • Growth: Opportunity to work and implement cutting-edge tools and technologies in a rapidly growing and evolving team and data landscape. • Culture: Work in a dynamic, collaborative and supportive environment where your ideas and growth are valued, encouraged, and expected.