Data Engineer, Belgium, 5-7 Yrs Exp

Description
Language : English
*Required Skills:*

  • 5-7 years or more work experience in Data Engineering
  • Develop multiple Data Warehouses, Data marts having various business logic with Microsoft
    SQL Server , AWS Redshift ,AWS RDS and PostgreSQL database.
  • Implement advanced concepts like OLTP and OLAP using Microsoft SQL Server, PostgreSQL,
    AWS Redshift and AWS RDS.
  • Manage and configured SQL databases like PostgreSQL, Microsoft SQL Server and AWS
    Redshift as an Administrator and Developer.
  • Implement Spark Parallelize across multiple spark nodes to help ingest and transform huge
    datasets using PySpark.
  • Import data from AWS S3 into Spark RDD.
  • Make use of Spark SQL to query data frames and Spark Session data.
  • Implement ETL framework using Spark with Python and loaded standardized data into Hive
    and HBase tables.
  • Develop various complex ETL flows including Data Extraction , Storing Data , Data
    Warehousing and Dimensional & Data Modeling with SSIS and Python (Pandas , Airflow and
    PySpark).
  • Make various migrations of databases from on-premises infrastructure to cloud based
    infrastructure for Microsoft SQL Server, PostgreSQL and Mongo DB databases .
  • Expert Knowledge on Mongo DB NoSQL Data modelling , tuning , disaster recovery and
    scaling.
  • Made use of PL/SQL scripts using shell scripts to clean , tune and load data into databases.
  • Made use of modules like pg_loader , bcp utility and timescale DB parallel copy during
    migrations to automate bulk data transfers.
  • Have made use of Shell Scripts to clean source files for consumption , scheduling of jobs and
    transfer of files from source to destination.
  • Extract , Load and Transform using PySpark , Spark SQL on Spark Clusters hosted on AWS
    and Databricks.
  • Optimization of existing Spark Clusters hosted on AWS and Databricks.
  • Developed multiple data visualization reports using Tableau , Power BI and SSRS with filters
    and charts.
  • Made use of advanced Power BI features like RLS with binning/grouping , outlier grouping to
    create user specific views.
  • Make use of M query to enable custom data ingestion as per customer requirement during
    dataset refresh of Power BI