数据工程师 - 高级 JP982
Data Engineer - Senior JP982
项目概述：
阿尔伯塔省政府（GoA）正在转变政府工作方式，以向阿尔伯塔人提供更简单、更高效和更好的服务。数字设计与交付（DDD）部门是GoA的现代数字交付中心，与各部合作设计和交付数字产品、平台和服务。DDD应用以用户为中心的设计、敏捷交付、现代数据实践和AI驱动的方法，以改善服务成果并推动政府的数字化转型。
在跨学科产品团队中，数据工程师将与业务和技术利益相关者合作，了解数据需求并开发现代数据解决方案。理想的候选人应具备扎实的数据工程实践基础，以及从复杂数据集中提取可操作见解所需的分析能力。
该职位支持数据解决方案的交付，包括数据管道、集成和迁移功能、数据模型、分析、报告和数据治理实践。通过结合技术专长与分析洞察力，数据工程师使各部门能够提高数据质量和可访问性，加强自助分析，并做出支持阿尔伯塔省政府现代数字服务交付的明智决策
服务范围：
数据工程师需全职工作，参与两（2）至三（3）个项目。工作时间、地点和频率将根据项目需求而变化。每个阶段结束时，预计数据工程师的工作时长最多为1,960小时，除非与省政府另有约定。然而，根据工作的性质和需求，数据工程师可能需要工作更少或更多的时间，具体由省政府指导。
服务和项目交付物应随着工作的进展而演变，以应对不断出现的用户和业务需求，以及不断变化的设计和技术机会。然而，在项目进行过程中，以下交付物必须迭代完成：
数据工程：
· 在本地和云平台（Azure、Databricks、Microsoft Fabric、GCP、AWS）上设计、构建和维护可扩展的数据管道，以摄取、转换和存储多样化的数据集，支持企业业务用例。
· 开发、优化和维护数据模型，包括维度
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Project Overview:
The Government of Alberta (GoA) has embarked on transforming the work of government to deliver simpler, more efficient, and better services for Albertans. The Digital Design and Delivery (DDD) division serves as the GoA's center for modern digital delivery, partnering with ministries to design and deliver digital products, platforms, and services. DDD applies human-centered design, agile delivery, modern data practices, and AI-enabled approaches to improve service outcomes and advance digital transformation across government.
Working within multidisciplinary product teams, Data Engineer(s) will collaborate with business and technical stakeholders to understand data requirements and develop modern data solutions. The ideal candidate will have a strong foundation in data engineering practices, combined with the analytical skills necessary to derive actionable insights from complex datasets.
The role supports the delivery of data solutions, including data pipelines, integration and migration capabilities, data models, analytics, reporting, and data governance practices. By combining technical expertise with analytical insight, Data Engineer(s) enable ministries to improve data quality and accessibility, strengthen self-service analytics, and make informed decisions that support the delivery of modern digital services across the Government of Alberta
Scope of Services:
The Data Engineer(s) will be required on a full-time basis, working across two (2) to three (3) projects. Time, location and frequency of work will vary depending on the needs of the project. At the end of each term, it is expected that the Data Engineer(s) may work a maximum of 1,960 hours, unless otherwise agreed upon with the province. However, Data Engineer(s) may be required to work fewer or more hours depending on the nature and needs of their work, as directed by the province.
Services and project deliverables should evolve as the work progresses in response to emerging user and business needs, as well as evolving design and technical opportunities. However, the following deliverables must be delivered iteratively throughout the course of the project:
Data Engineering:
· Design, build, and maintain scalable data pipelines across on-premises and cloud platforms (Azure, Databricks, Microsoft Fabric, GCP, AWS) to ingest, transform, and store diverse datasets in support of enterprise business use cases.
· Develop, optimize, and maintain data models, including dimensional models (star and snowflake schemas), to improve query performance, scalability, and usability for analytics and reporting.
· Integrate data from a variety of sources, including relational databases, NoSQL platforms, APIs, and files, applying AI-enabled data integration techniques such as intelligent data mapping, schema discovery, metadata enrichment, and automated data quality validation to improve accuracy and efficiency.
· Enhance ETL/ELT processes through optimization, automation, and performance tuning to improve scalability, reduce bottlenecks, and support high-volume data processing.
· Develop and operate end-to-end ETL/ELT workflows using tools such as SSIS, Azure/Fabric Data Factory, Dataflows, and Notebooks, incorporating data validation, error handling, logging, monitoring, and scheduling to ensure reliable data operations.
· Automate data pipeline deployment and operations through CI/CD practices, including automated testing, release management, and monitoring to enable faster and more reliable delivery.
· Support the management and governance of enterprise data platforms, including data lakes, data warehouses, security controls, and access management.
· Partner with architects, developers, and stakeholders to translate requirements into solutions, and prepare curated data marts and fact/dimension tables to support analytics.
Data Analytics:
· Analyze datasets to identify trends, patterns, and anomalies. Use statistical methods, DAX, Python, and R to generate insights that inform business strategies.
· Develop interactive Power BI dashboards and reports, leveraging DAX to create calculated columns and measures, monitor key performance indicators, deliver service dashboards, and communicate results effectively to stakeholders.
· Build predictive or descriptive models using statistical, Python, or R-based machine learning methods. Design and integrate data models to improve service delivery.
· Present findings to non-technical audiences in clear, actionable terms. Translate complex data into business-focused insights and recommendations.
· Deliver analytics solutions iteratively in an Agile environment. Mentor teams to enhance analytics fluency and support self-service capabilities.
· Provide data-driven analysis, visualizations, and AI-enabled insights to support corporate priorities, strategic initiatives, and informed decision-making.
The province and the Contractor shall determine changes to Services and Materials as required. The province and the Contractor will determine changes to Services and Materials through the Artifacts.
Originally posted on Himalayas