Data Quality Engineer

1 day ago


Helsinki, Uusimaa, Finland Huawei Full time
Company Overview
Huawei Consumer Business Group is a leader in AI life solutions, covering smartphones, PC and tablets, wearables, mobile broadband devices, family devices, and device cloud services. Our mission is to deliver the latest technologies to consumers and share the happiness of technological advances with more people around the world.

Job Description
You will play a key role in transforming raw data into actionable insights by designing and implementing efficient data pipelines as a Data Quality Engineer. Your responsibilities will include data extraction, ingestion, cleaning, preprocessing, annotation, quality assurance, analysis, and collaboration with data scientists and domain experts.

Key Responsibilities
  1. Data Extraction and Ingestion:
    • Develop efficient data pipelines.
    • Ensure data quality and completeness during the ingestion process.
  2. Data Cleaning and Preprocessing:
    • Cleanse, transform, and enrich data to eliminate errors, inconsistencies, and missing values.
    • Standardize data formats and structures to facilitate analysis.
  3. Data Annotation and Labeling:
    • Annotate and label data for machine learning and AI projects.
    • Develop and maintain annotation guidelines and quality control procedures.
  4. Data Quality Assurance:
    • Implement robust quality checks and monitoring to maintain data integrity.
    • Identify and resolve data quality issues proactively.
  5. Data Analysis and Exploration:
    • Conduct exploratory data analysis to uncover patterns, trends, and anomalies.
    • Visualize data to communicate insights effectively.
    • Develop browser plugins and visualization tools for internal users.
  6. Big Data Processing:
    • Work with big data technologies (e.g., Hadoop, Spark) to process large datasets efficiently.
    • Optimize data pipelines for performance and scalability.
  7. Natural Language Processing:
    • Apply NLP techniques to extract insights from textual data.
    • Develop language models and text analysis tools.
  8. Data Collaboration:
    • Collaborate with data scientists, analysts, and domain experts to understand their data needs.
    • Communicate data insights effectively to stakeholders.


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