Data Systems Engineer

Data Systems Engineer Location: Poland Street, London (2 days minimum in office) Why choose Tag? We are the long-standing, production partner of choice that has helped brands across borders and cultures for over half a century achieve their business goals. With 2,700 experts in 29 countries across the world, we are a global team of collaborators, innovators, and motivators. We pride ourselves on creating empowering, safe, and supportive environments for all our employees, regardless of race, gender, sexual orientation, ability, or any other defining factor. We embrace difference through diversity of thought, experience, and expertise to maximise potential and bring the greatest benefits for our people and our clients. We can’t bring big ideas to life without exceptional people. At Tag, we respect individuality and the power of the collective. We want people to be themselves, unafraid to voice ideas, no matter how big they are or who they come from. In June 2023, Tag was acquired by dentsu Group, Inc, though we remain a distinct brand that is led by David Kassler, Tag Global CEO, headquartered in London. As dentsu’s acquisition of Tag significantly expands content delivery capabilities, Tag’s expertise to deliver personalized, omnichannel content in real-time and at-scale for clients remains unparalleled across the entire customer journey, unlocking marketing effectiveness and efficiency. Tag and dentsu bring together our innovation and technology infrastructure to help solve clients’ toughest challenges. United in business acumen, we share similar core values, company culture, and embrace a vision “to be at the forefront of people-centric transformations that shape society. dentsu was founded over 120 years ago and proudly counts nearly 72,000 employees around the world. Responsibilities Act as the primary steward of data across all our applications, understanding and managing various data types and sources Design, implement, and maintain scalable data pipelines and ETL processes Develop and manage data integrations between different systems and applications Perform complex data analysis to derive insights and support decision-making Build and maintain dashboards for data visualization and reporting Prepare and manage datasets for machine learning and analytics projects Collaborate with cross-functional teams to understand data needs and provide solutions Implement data quality checks and ensure data integrity across systems Optimize data storage and retrieval for performance and cost efficiency Stay current with emerging technologies and best practices in data engineering and analytics Skills and Experience required Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field 4 years of experience in data engineering, data science, or a similar role Proven track record of building and maintaining data systems at scale Strong analytical and problem-solving skills Excellent communication skills, able to translate complex technical concepts to non-technical stakeholders Required Technical Skills: Strong proficiency in Python or another relevant programming language for data manipulation and analysis Experience with big data technologies (e.g., Hadoop, Spark) Expertise in SQL and working with relational databases (e.g., PostgreSQL, MySQL) Familiarity with NoSQL databases (e.g., MongoDB, Cassandra) Experience with data warehousing solutions and ETL tools Proficiency in building data pipelines and workflow management tools (e.g., Airflow, Luigi) Knowledge of data visualization tools (e.g., Tableau, PowerBI, or similar) Understanding of statistical analysis and machine learning concepts Experience with cloud platforms (AWS, GCP, or Azure) for data processing and storage Nice to Have: Experience with stream processing technologies (e.g., Kafka, Flink) Knowledge of data modeling and dimensional modeling concepts Familiarity with data governance and compliance requirements Experience with containerization and orchestration (e.g., Docker, Kubernetes) Understanding of data security best practices Exposure to machine learning operations (MLOps) practices Key Attributes: Passionate about data and its potential to drive business value Detail-oriented with a strong focus on data quality and integrity Proactive in identifying and solving data-related challenges Adaptable and quick to learn new technologies and methodologies Collaborative mindset, able to work effectively with diverse teams Self-motivated and able to manage multiple priorities in a fast-paced environment As an ethical employer, Tag will never ask job applicants to provide private, sensitive information upfront or make offers of employment contingent on financial requests or responsibilities from any candidate.