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Remote mid-level generalist data engineer role with popular title and broad requirements.
Data engineering skills (Python, Spark, AWS, SQL) are highly transferable across industries.
Explicit 4-6 years plus mandatory AWS, Spark, Python, and data modelling requirements.
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Own end-to-end design, construction, testing, and maintenance of scalable ETL/ELT data pipelines using Python, Spark, and PySpark.
Architect and optimize distributed Big Data processing systems leveraging Apache Spark and Hadoop within the AWS ecosystem.
Build and manage cloud-native data architectures, including data lakes and warehouses, using AWS services like S3, EMR, Glue, Redshift, Lambda, and Athena, ensuring data quality, governance, and security.
4 to 6 years of professional experience in data engineering or software development.
Hands-on experience with AWS cloud platforms, specifically building data lakes and warehouses.
Proficiency with Big Data frameworks such as Apache Spark and streaming technologies like Kafka or Kinesis.
Strong coding skills in Python and advanced SQL expertise.
Experienced in architecting scalable data pipelines and distributed data processing in a cloud environment, especially AWS.
Skilled in dimensional data modeling and optimizing large-scale analytics workloads.
Familiar with infrastructure automation (Terraform/CloudFormation) and orchestration tools (Apache Airflow/Step Functions) is a plus but not mandatory.