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- Collaborate on building secure ETLs, application, data integrations, data workflows, data pipelines, API’s and automate data preparation jobs for analysis, consumptions, BI & reporting, delivering data to other application and platforms. Optimize data flow and processing performance, secure sharing, automated monitoring and alerts.
- Utilize data prep guidelines in accordance to standards, mature data engineering best practices and serve as a subject matter expert in data engineering. Collaborate with other teams on data engineering capabilities, awareness and develop secure solution to meet their requirements.
- Engage with internal/external partners to gather requirements and collaborate with technology engineers, architects and data scientists to help leader in designing a robust data enablement solution.
- Produce deliverables and identify opportunities to shape robust data ingestions, data processing and data delivery capabilities that inform and inspire internal stakeholders and decision makers.
- Understands and actively participates in Environmental, Health & Safety responsibilities by following established UO policy, procedures, training and team member involvement activities.
- Performs other duties as assigned.
- 3+ years of experience in C/C/C# or Java or Python, PL/SQL, Database design and knowledge of Postgres extensions, relational databases and Cloud infrastructures.
- Strong familiarity with ETL automation, embedded SQL, RDMS.
- Knowledge of unstructured data and NoSQL is desired.
- ; or equivalent combination of education and experience.
- Required:
- 3+ years of experience in C#/Java, Python, PL/SQL, database design and hands-on experience in developing automated data pipelines using C#/Java/Python or any other programming language and data integration with cross platforms using APIs.
- Ability to create ETL tools, optimize queries and advanced SQL scripting in PostgreSQL, SQL Server and Snowflake
- Knowledge of AWS Infrastructure, services and tools: Amazon S3, Amazon EC2, Amazon Redshift and cognitive services. Experience of data ingestions in raw bucket, developing a process of moving into curated bucket and developing conform and consumption layers.
- Familiarity and knowledge of Big Data platforms and architecture: Hadoop, Spark, BigQuery, Datalake. Clear understanding of RDBMS and NoSQL.
- Experience in integration and data delivery to BI platforms.
- Experience in developing data engineering standards, best practices and optimization techniques. Clear understanding of data security practices, retention & archival practices, encryption techniques, data in transit and data at rest security protocols.
- Experience in automating jobs, ability to leverage command line utilities and able to support API builds on premise and cloud environments
- Ability to develop automated data (structured and unstructured) and conduct feature engineering and business rules for analysis and modeling, meeting standardization requirements and focused on efficiency, scalability and reproducibility.
- Experience defining, documenting and developing best practices, processes and standards related to data engineering for advanced analytics purpose
- Preferred:
- Extensive experience in developing automated data pipeline, workflows, intelligence-based monitoring, alerts and reporting and ability to work with data architects, scientists, BI SMEs developing complex data enablement solutions.
Your talent, skills and experience will be rewarded with a competitive compensation package.
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