Data Engineer, Data Enablement and Platform Mgmt
JOB SUMMARY: Responsible for building automated data processes for consuming data from different data sources and process data for various consumption and deliver data to target state, platform or department as appropriate. Design data ingestion techniques and build efficient and scalable data applications through use of APIs, Python, Spark, and Data Management best practices and build futuristic and scalable AWS cloud-based data analytics platform to support digital and social transformation at UPR. The data engineer will develop application, integrate data flows cross platforms, build data pipeline, prepare and process data (structured and unstructured) and deliver data to meets requirements and focuses on efficiency, scalability and reproducibility. The data engineer will also help define, optimize and automate data and process strategy using a variety of techniques for media measurement. The data engineer will also be responsible to collaborate with data & audience science, BI & reporting, IT and data tools for data ingestion, processing and delivery.
- 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.
EDUCATION: Bachelor's degree in Computer Science/Engineering/Data Science or in a relevant applied quantitative field is required.
- 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.
- 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
- 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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