Senior Data Engineer

November 26

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Valtech

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B2B • Marketing • SaaS

Valtech is a global digital agency focusing on experience innovation. They strive to transform businesses through a combination of technology, marketing, and data strategies. Valtech helps companies elevate their digital presence and drive commerce strategies, enhance enterprise digital transformations, and unlock marketing and performance potential. They also utilize data and AI to help organizations harness the power of information. With offices around the world, Valtech partners with businesses to shape their digital futures, offering a range of services and insights designed to enhance customer experiences.

5001 - 10000 employees

Founded 1997

🤝 B2B

☁️ SaaS

📋 Description

• Design, build, and maintain highly scalable, reliable, and efficient ETL/ELT pipelines for batch and real-time data processing using various tools and technologies. • Ingest data from a multitude of sources, including relational databases, APIs, streaming services, cloud storage, and third-party data providers. • Transform raw data into clean, structured, and AI/ML-ready formats for consumption by data scientists and machine learning models. • Monitor data pipelines for performance, integrity, and security, troubleshooting and resolving issues as they arise. • Contribute to the design and evolution of the data architecture, including data lakes, data warehouses, and data marts, ensuring they meet the demands of AI workloads. • Implement and optimize data storage solutions (e.g., S3, Azure Data Lake Storage, BigQuery, Snowflake) and processing frameworks (e.g., Apache Spark, Flink, Kafka). • Ensure data security, privacy, and compliance with internal policies and external regulations (e.g., GDPR, CCPA). • Evaluate and integrate new data technologies and tools to enhance our data platform capabilities. • Work closely with data scientists, machine learning engineers, and business analysts to understand their data needs and provide tailored data solutions. • Enable efficient data access for model training, validation, and deployment, including feature engineering and data versioning. • Support the MLOps lifecycle by providing robust data foundations for automated model retraining and monitoring. • Optimize data processing for machine learning algorithms, ensuring high performance and cost efficiency. • Implement and enforce data quality checks and validation processes within pipelines to maintain high data integrity. • Develop and maintain comprehensive data documentation, including data dictionaries, data lineage, and metadata management. • Contribute to data governance initiatives, ensuring consistent data definitions and usage across the organization.

🎯 Requirements

• Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or a related quantitative field. • Minimum of 3-5 years of experience as a Data Engineer, with significant experience in building and managing data platforms. • Proven experience working on projects that directly support Artificial Intelligence, Machine Learning, or Data Science initiatives. • Strong proficiency in at least one modern programming language (e.g., Python, Scala, Java). Python is highly preferred. • Extensive experience with big data processing frameworks and stream processing. • Hands-on experience with GCP’s data services (e.g.,BigQuery, Dataflow, Dataproc). • Solid understanding of SQL and experience with various database systems (e.g., PostgreSQL, MySQL, SQL Server, NoSQL databases like MongoDB, Cassandra). • Experience with data warehousing concepts and technologies (e.g., Google BigQuery). • Experience with workflow orchestration tools (e.g., Airflow, Prefect, Luigi). • Proficiency with Git and collaborative development workflows. • Good understanding of data modeling, data warehousing principles, and data lake architectures. • Familiarity with machine learning concepts, MLOps principles, and how data pipelines feed into AI models. • Excellent analytical and problem-solving skills, with a keen eye for detail and data accuracy. • Strong communication skills, able to articulate complex technical concepts to both technical and non-technical stakeholders. • Ability to work effectively in an Agile team environment, collaborating with cross-functional teams. • Advanced English-Level communication skills.

🏖️ Benefits

• Flexibility, with remote and hybrid work options (country-dependent) • Career advancement, with international mobility and professional development programs • Learning and development, with access to cutting-edge tools, training and industry experts