OPEN: Data Engineer (Databricks)

Job Overview

• As a Data Engineer, you will be responsible for designing, building and supporting the data driven applications which enable innovative, customer centric digital experiences.
• You will be working as part of a cross-discipline agile team who help each other solve problems across all business areas.
• You will build reliable, supportable & performant data lake & data warehouse products to meet the organization’s need for data to drive reporting analytics, applications, and innovation.
• You will employ best practice in development, security, accessibility and design to achieve the highest quality of service for our customers.
• Design, build and productionize modular and scalable data ELT/ETL pipelines and data infrastructure leveraging the wide range of data sources across the organization
• Implement curated common data models that offer an integrated, business-centric single source of truth for business intelligence, reporting, and downstream system use, in collaboration with Data Architect
• Work closely with infrastructure and cyber teams to ensure data is secure in transit and at rest
• Clean, prepare and optimize datasets for performance, ensuring lineage and quality controls are applied throughout the data integration cycle
• Support Business Intelligence Analysts in modelling data for visualization and reporting, using dimensional data modeling and aggregation optimization methods
• Troubleshoot issues related to ingestion, data transformation and pipeline performance, data accuracy and integrity
• Collaborate with business analysts, data scientists, data engineers, data analysts and solution architects to develop data pipelines to feed our data marketplace
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Work with tools in the Microsoft Stack; Azure Data Factory, Azure Data Lake, Azure SQL Databases, Azure Data Warehouse, Azure Synapse Analytics Services, Azure Databricks, Microsoft Purview, and Power BI
• Work within the agile SCRUM work management framework in delivery of products and services, including contributing to feature & user story backlog item development, and utilizing related Kanban/SCRUM toolsets
• Assist in building data catalog and maintenance of relevant metadata for datasets published for enterprise use
• Develop optimized, performant data pipelines and models at scale using technologies such as Python, Spark and SQL, consuming data sources in XML, CSV, JSON, REST APIs, or other formats
• Document as-built pipelines and data products within the product description, and utilize source control to ensure a maintainable code-base
• Implement orchestration of data pipeline execution to ensure data products meet customer latency expectations, dependencies are managed, and datasets are as up-to-date as possible, with minimal disruption to end-customer use
• Create tooling to help with day to day tasks, and reduce toil via automation wherever possible
• Work with Continuous Integration/Continuous Delivery and DevOps pipelines to automate infrastructure, code delivery and product enhancement isolation and proper release management and versioning
• Monitor the ongoing operation of in-production solutions, assist in troubleshooting issues, and provide Tier 2 support for datasets produced by the team, on an as-required basis
• Implement and manage appropriate access to data products via role-based access control
• Write and perform automated unit and regression testing for data product builds, assist with user acceptance testing and system integration testing as required, and assist in design of relevant test cases
• Participate in peer code review sessions, and approve non-production pull requests

Qualifications

• Experience as a Data Engineer in a Big Data environment specifically within Databricks and Azure.
• Experience with integrating structured and unstructured data across various platforms and sources.
• Knowledge of content fragmentation, partitioning, query parallelism, and query execution plans.
• Experience with implementing event-driven (pub/sub), near-real-time, or streaming data solutions.
• Strong knowledge with programming methodologies (source/version control, continuous integration/continuous delivery, automated testing, quality assurance) and agile development methodologies.
• In depth knowledge of and experience with relational, SQL and NoSQL databases.
• Fluency with SQL, Python, and Spark/PySpark is required.
• A period of over 4 years and up to including 6 years in data modeling, data warehouse design and data solution architecture in a Big Data environment.

CPUS Engineering employs artificial intelligence (AI) tools as part of its recruitment process to enhance efficiency and consistency. These tools may assist with activities such as application screening, candidate evaluation, and summarization of interview feedback.

AI-generated outputs are intended to supplement, not replace, human judgment. All final hiring decisions are made by qualified human decision-makers who exercise professional discretion in reviewing and assessing candidate information alongside any AI-generated insights.

CPUS Engineering ensures that the use of AI in recruitment complies with all applicable employment laws, human rights legislation, and privacy regulations. The organization is committed to maintaining fairness, transparency, and non-discrimination throughout the hiring process.