Data Engineer

6 days ago


Limassol, Cyprus Muse Group Full time

Muse Group empowers music makers. We create the world’s most popular apps for playing, recording and composing music. Through our innovative learning tools, expansive music catalogs and free open-source software, we make it easier for millions — from beginners to experienced musicians — to be creative every day.

Our talented team of music lovers collaborates all over the globe, from Limassol to Seoul and Boston to Berlin. We’re an ambitious company with the drive and culture of a startup, with many more exciting Muse Group developments to come.

We are looking for a Data Engineer to drive our data transformation. Utilize models from classic ML to cutting-edge neural networks on groundbreaking projects. Participate in infrastructure setup and grow with our dynamic team. Convert raw data into valuable insights, ensuring consistency, accuracy, and accessibility for informed decision-making.

KEY RESPONSIBILITIES

Architectural Design for Data Integration:

  • Construct and manage the architecture for data integration solutions, ensuring that it aligns with and supports business requirements.
  • Design scalable, reliable, and efficient data structures and integration processes to facilitate effective data collection, storage, and retrieval.

Development of Data Solutions:

  • Develop and implement new data solutions including ETL processes, data pipelines, integration services, batch and stream processing, and data enrichment services.
  • Innovate and optimize data management techniques to enhance data accessibility and utility.

Maintenance and Transition of Data Platforms:

  • Maintain existing data solutions while concurrently developing a new, more robust data platform.
  • Manage the transition from the current architecture to the target architecture smoothly, ensuring continuity and minimizing disruption to data services.

Infrastructure Building for Data:

  • Build and maintain the infrastructure required for effective data storage, processing, and provisioning to internal and external customers.
  • Enable diverse teams to work with data efficiently, providing capabilities for data processing, transformation, merging, ad-hoc querying, and analysis.

Dataset Preparation:

  • Prepare and aggregate datasets from various sources as per specific requests, ensuring data is clean, structured, and integration-ready.

Monitoring and Service Level Agreements (SLAs):

  • Establish and maintain monitoring systems for data delivery and processing.
  • Define and implement SLAs, and analyze metrics to ensure compliance and performance standards are met.

Data Reconciliation Solutions:

  • Develop built-in solutions for data reconciliation to ensure data integrity and accuracy across systems and processes.

Management of Large and Complex Data Sets:

  • Assemble large and complex data sets that adhere to both functional and non-functional business requirements.
  • Ensure datasets are conducive to addressing business cases and can be effectively used for strategic decision-making.

Improvement Recommendations:

  • Continuously assess data systems and processes, recommending ways to enhance data reliability, efficiency, and quality.
  • Advocate for and implement best practices in data management, staying abreast of industry trends and technological advancements to drive continuous improvement.

REQUIRED EXPERIENCE

Hard Skills:

Database Design:

  • Proficiency in designing databases with massively parallel processing (MPP) appliances such as Druid, BigQuery, ClickHouse, PostgreSQL, and similar technologies.

Experience in Deploying Code:

  • Minimum of 4-5 years hands-on experience in deploying production-quality code.

Programming Languages:

  • Professional experience in using Python, Java, or Scala for data processing, with a preference for Python.
  • Familiarity with data-related Python packages.

ETL/ELT Implementation:

  • Hands-on experience in implementing ETL (or ELT) best practices at a large scale.

Streaming and Data Platform Technologies:

  • Experience with streaming technologies such as Kafka, and data platform technologies including ClickHouse, MongoDB.

Data Pipeline Tools:

  • Hands-on experience with data pipeline and workflow management tools such as Airflow, Luigi, Azkaban, and dbt.

SQL Expertise:

  • Expertise in structured query language (SQL), including knowledge of query optimization techniques.

Version Control:

  • Proficiency in using Git Distributed Version Control System (DVCS).

DevOps and Agile Practices:

  • A strong understanding of and commitment to DevOps culture, including continuous integration and continuous delivery, as well as an Agile mindset.

Soft Skills:

Attention to Detail:

  • Highly detail-oriented, with a strong emphasis on data quality and the stability of data architecture.

Deadline Management:

  • Ability to work effectively under strict deadlines.

Teamwork and Relationship Building:

  • Skilled at dealing with constructive criticism and proficient in building relationships within the team to achieve common goals.

Communication Skills:

  • Practice attentive listening, and be adept at persuasion and argumentation, with a knack for clear and effective communication.
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