Data Engineer

2 months ago


Remote, Czech Republic Alcor Full time

Important technologies:

  • Expert understanding of Data pipeline design and implementation using Databricks, and Python (or Python derivative, like PySpark).
  • Expert visualization skills using Sisense and/or other visualization tools.
  • Expert with SQL.
  • Expert understanding of Data management and/or Data governance (making sure the data is of expected volume, schema, etc.)
  • Experience with modern cloud-based Data Engineering on AWS or other cloud platforms.

Nice to have:

  • Experience with Kedro on Databricks.
    Experience in designing and implementing Data mesh.
  • Exposure to Airbyte reading from multiple different data sources.
  • Good understanding of microservices architecture principle.

If that's you, apply to us Let's create together

,[be accountable for:, ○ design and implementation of features and services for our data lake, ingestion and distribution systems,, ○ availability and performance of our data lake, ingestion, and distribution systems,, ○ design and implementation of ingestion, enrichment, and transformation pipelines., Lead system design activities aimed to create clarity, alignment, and ascertain feasibility, Work across the full stack depending on where you can drive the highest impact: from ETL pipelines to data warehousing to visualizations, as well as testing and cloud infrastructure., Use your experience in data management and governance to define and implement best practices across our systems ensuring security, consistency and quality across all of our data., Develop and optimize Spark jobs, notebooks, and data pipelines within the Databricks environment, Work across the full stack depending on where you can drive the highest impact: from ETL pipelines to data warehousing to visualizations, as well as testing and cloud infrastructure., Use your experience in data management and governance to define and implement best practices across our systems ensuring security, consistency and quality across all of our data., Develop and optimize Spark jobs, notebooks, and data pipelines within the Databricks environment, Work with the Chief Architect and ML engineers to define an information architecture capable of enabling easy implementation of data models and catalogues within the data lake, ensuring data quality, integrity, and optimal performance., Train/mentor other engineers inside and outside of the team on Data engineering best practices., Frequently review system, delivery, and quality metrics and drive the right initiatives to improve them, ensuring long-term quality, scalability, and maintainability of our systems., Set the bar for what good quality data looks like, and use your expert knowledge to train and mentor other engineers to improve data quality.] Requirements: Python, SQL, Databricks, Snowflake, ETL, Cloud, Spark, Airflow

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