The Data Engineer will serve as a vital resource within the Enterprise Data Group 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:

1. 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

2. 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.

3. 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.

4. 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 big data, cloud (AWS, Azure), Databricks and Power BI to drive innovation.

Education:

Graduate of Computer Science, Information Technology, Data Engineering, Statistics, or related technical disciplines.

Work Experience:

3 to 5 years of practical experience in cloud data lakehouse design and implementation with open table formats (Delta Lake, Apache Iceberg)
Working experience with AWS data services - Athena, Data Pipelines, DynamoDb, Glue, Lambda, Redshift, S3, Step Functions - and other AWS services - CloudFormation, CloudWatch, IAM, security-related services ○ At least 2 years extensive and end-to-end reporting on Power BI
Experience with dbt and Apache Airflow are a plus

Technical Competencies (Required)

Core Programming & QueryingLanguages:
Strong proficiency in Python and SQL for data manipulation and analysis.
Version Control: Hands-on Git experience within collaborative project environments
Data Architecture & Distributed Computing
Big Data Frameworks: Hands-on experience building and optimizing enterprise-scale data and processing pipelines using Apache Spark.
Modern Lakehouse Architectures: Working and applied knowledge of the Medallion Architecture implemented on data lakehouses via Delta Lake and/or Apache Iceberg.
Data Modeling: Strong understanding of Dimensional Data Modeling, with a strict preference for Star Schema design.
Pipeline Infrastructure & Engineering
Orchestration: Experience in pipeline orchestration design, scheduling, and implementation. -Cloud Infrastructure & Storage: Working knowledge of cloud storage management (e.g., AWS S3, Azure ADLS).
Business Intelligence & Dashboarding
Semantic Layer Design: Hands-on experience building and managing Power BI semantic models.
Report Development: Working knowledge of DAX, Power Query, and M for robust report generation.
Data Ingestion for BI: Proven experience integrating Power BI with diverse, heterogeneous data sources.

Functional Competencies (Required)

Data Engineering Design Patterns
Ingestion & Processing: Applied understanding of various data ingestion patterns (batch, micro-batch) and downstream processing workflows.
Data warehouse patterns: Working knowledge of pipeline patterns that address special business process / operational scenarios
Workflow Automation: Ability to design end-to-end processing pipelines that are modular, resilient, and optimized for performance.
Data Security, Governance & Access Control
Identity & Access Management (IAM): Practical application of cloud security configurations using AWS IAM and Azure IAM/Active Directory.
Granular Access Frameworks: Ability to implement security architectures including Row-Level Security (RLS), Attribute-Based Access Control (ABAC), Group-Based Access Control (GBAC), and Role-Based Access Control (RBAC)
Quality Assurance & Reliability
Structured Testing: Execution of structured testing methodologies specifically tailored for ETL and ELT pipelines to ensure data integrity, schema validation, and catch regressions.

Preferred qualifications

Advanced Infrastructure & DevOps
Infrastructure-as-Code (IaC): Experience managing infrastructure via code (e.g., AWS CloudFormation, Databricks Asset Bundles (DAB)).
BI DevOps: Understanding of deployment pipelines and CI/CD practices within the Power BI Service.
Next-Gen Platforms & Governance
Databricks Ecosystem: Experience with modern Databricks pipeline development utilizing Lakeflow technologies.
Data Lifecycle Management: Conceptual or hands-on experience with Metadata Management across enterprise data platforms.
Professional Certifications
Databricks: Databricks Certified Data Engineer Associate or Professional.
AWS: AWS Solutions Architect, Data Engineer, or Data Analytics certifications.
Azure: Azure Data Engineering certifications (e.g., DP-203)

Culture Fit: Demonstrates Pointwest's culture of Agility, Accountability, Innovation, Collaboration, and Customer Centricity.

Data Engineer (mid-level)