Data Engineer

The Data Engineer will serve as a vital resource within the Enterprise Data Office (EDO) to design, build, and maintain the full analytics delivery stack. This role involves transforming raw data into actionable insights by developing robust data models, optimizing ETL/ELT pipelines, and creating high-impact BI products. Operating as a capable individual contributor, the engineer will lead technical delivery for specific projects while adhering to Pointwest's core values of Leadership, Excellence, and Innovation

Key Responsibilities

Delighting Our Customers & Stakeholders
Work closely with business units to understand analytic requirements and translate them into technical designs.
Create dashboards, reports, and data visualizations using Power BI to support organizational decision-making.
Manage stakeholder expectations and communicate delivery progress clearly and consistently.
Ensure the reliability and consistency of published data outputs to maintain stakeholder trust.
Growing Our Business
Develop, optimize, and monitor data transformation workflows from source systems to semantic layers to enhance data accessibility.
Enable seamless integration with Databricks and other cloud platforms to support analytics performance and scalability.
Contribute to enterprise-wide analytics initiatives that drive business value and strategic growth.
Improving the Way We Work
Build reusable and well-documented data models using dbt and SQL following defined team standards.
Implement version control (Git), CI/CD practices, and rigorous testing for all analytics assets.
Troubleshoot and resolve pipeline issues independently to ensure continuous data flow.
Participate in data governance and metadata enrichment activities to improve data quality.
Developing Myself and Others
Support peer reviews and knowledge-sharing efforts within the IDEA team.
Maintain an agile delivery mindset, adapting to fast-paced iteration cycles.
Stay current with industry trends in Databricks, dbt, and Azure Synapse to drive innovation

Key Metrics for Success

Stakeholder Satisfaction Score: Feedback from business units on the clarity and utility of BI products.
Requirement Accuracy: Percentage of technical designs that successfully meet initial business requirements.
Pipeline Scalability: Successful integration and performance of data platforms like Databricks.
Insight Delivery Speed: Reduction in time-to-market for new analytics products.
Data Quality & Reliability: Number of issues found in peer reviews or testing cycles.
System Uptime: Success rate of ETL/ELT pipelines and resolution time for technical incidents.
Documentation Completion: Adherence to metadata and technical documentation standards.
Collaboration Rating: Peer and lead assessment of contribution to knowledge sharing and teamwork.
Skill Proficiency: Progress toward or attainment of preferred certifications (DBT, Power BI, Azure).

Qualifications:

Education: Graduate of Computer Science, Information Technology, Data Engineering, Statistics, or related technical disciplines.
Experience: 3 to 8 years of hands-on experience in data engineering, analytics delivery, or dashboarding.
Technical Skills:
Strong proficiency in SQL, dbt, and Power BI.
Expertise in ETL/ELT frameworks and dimensional modeling.
Experience with Git, CI/CD, and cloud platforms (Databricks, Azure Synapse).
Certifications (Preferred):
dbt Certification, Power BI, Databricks, or Azure Data Engineering.
Culture Fit:
Demonstrates Pointwest's culture of Agility, Accountability, Innovation, Collaboration, and Customer Centricity.

Data Engineer (Databricks, AWS)

Data Engineer