8 of 8 Permanent Amazon Athena Jobs in the North West

dynamic environment Desirable Experience • Exposure to IoT data ingestion and stream processing (MQTT, Kafka, Kinesis, etc.) • Familiarity with data lake/analytics environments (S3, Athena, Snowflake, BigQuery) • Experience with security frameworks (CIS Benchmarks, NIST, SOC2) • Previous work in regulated industries (water, utilities, energy, or environmental monitoring) • Certification: AWS Certified

years of experience in data engineering or a similar role. • Hands-on experience with core AWS data services (for example S3, Glue, Athena, Lambda, IAM, EMR). • Strong SQL skills (joins, window functions, optimization). • Solid Python for data processing. • Experience building production ETL/ELT pipelines. • Working knowledge

/DevOps environments Broad architectural understanding across enterprise, networks, data, and security domains Experience working across both AWS and Azure cloud environments (S3, Redshift, Athena, Glue, MSK, Entra ID, Azure SQL, Data Factory, Integration Services) Strong integration and API expertise (including REST) Proven ability to produce detailed high-level

/DevOps environments Broad architectural understanding across enterprise, networks, data, and security domains Experience working across both AWS and Azure cloud environments (S3, Redshift, Athena, Glue, MSK, Entra ID, Azure SQL, Data Factory, Integration Services) Strong integration and API expertise (including REST) Proven ability to produce detailed high-level

complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools … with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena/Redshift, etc.) is expected.

complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools … with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena/Redshift, etc.) is expected.

complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools … with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena/Redshift, etc.) is expected.

complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools … with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena/Redshift, etc.) is expected.