Data Systems Engineer

2 weeks ago

London, Greater London, United Kingdom Tag Full time
Data Systems Engineer Job Description

At Tag, we are seeking a highly skilled Data Systems Engineer to join our team. As a Data Systems Engineer, you will be responsible for designing, implementing, and maintaining scalable data pipelines and ETL processes. You will also develop and manage data integrations between different systems and applications, perform complex data analysis to derive insights and support decision-making, and build and maintain dashboards for data visualization and reporting.

Key Responsibilities:
  • Design and implement 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

Requirements:
  • 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

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

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