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описание
Jacobs solves critical problems for cities, environments, government, defence, regulated industries, scientific discovery and advanced manufacturing. Its Data & Digital team develops modern data platforms that help organisations use data more effectively and make informed decisions.
задачи
Design, build and maintain reliable ETL/ELT data pipelines using AWS or Azure cloud-native services and established engineering patterns;
Develop and support data integration components and services, including Java-based microservices where required;
Prepare, transform and validate structured and semi-structured data, including parquet, avro and JSON formats;
Write efficient SQL to query, validate and optimise data across cloud-based analytical platforms such as Athena, Redshift and Azure Synapse;
Build scalable data processing solutions using PySpark, Python and cloud-native orchestration services;
Implement data quality checks, validation processes, monitoring and logging to ensure trusted and reliable data delivery;
Support performance tuning and optimisation of data pipelines, SQL workloads and integration services;
Work with AWS services such as S3, Glue, Lambda, Athena and Step Functions, or Azure equivalents including Data Lake Storage Gen2, Data Factory, Synapse, Azure Functions and Logic Apps;
Contribute to documentation, runbooks, data dictionaries and reusable engineering assets that support delivery teams;
Collaborate with data architects, analysts, software engineers and client stakeholders to deliver high-quality solutions;
Support DevOps ways of working through source control, automated testing and CI/CD practices;
Communicate progress, risks and technical decisions effectively with technical and non-technical stakeholders.
требования
Experience with AWS Glue, Lambda, Step Functions or Azure Data Factory, Synapse Pipelines, Azure Functions and Logic Apps;
Experience with PySpark;
SQL experience with Athena, Redshift or Azure Synapse SQL;
Experience with AWS S3 or Azure Data Lake Storage Gen2;
Proficiency in Python and/or Java;
Experience developing ETL/ELT data pipelines;
Knowledge of data quality, validation and monitoring;
Experience with Git, CI/CD and Agile delivery practices;
Eligibility for SC Clearance;
Nice to have: No additional preferred qualifications specified.
условия
Flexible working arrangements;
Investment in development, including certifications, learning time and access to mentors;
Opportunities to contribute to communities of practice and internal technical initiatives;
Competitive benefits package including pension, holiday allowance and additional perks.
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