Data Developer

Tundra Oil & Gas Limited • Calgary, AB

4d • Hybrid

About The Position

Tundra is excited to share an opportunity for a Data Developer based out of the Calgary, AB, or Winnipeg, MB Office. The main responsibility of the Data Developer is to support the development and operation of the data pipelines and systems that ensure reliable, governed, and scalable data operations. Positioned within the IT department, this role ensures that data systems are engineered using sound software engineering principles and enterprise standards to effectively support the organization’s evolving business data requirements. Operating as the primary engineering partner to the Data Architect, the Data Developer ensures architectural patterns, platform standards, and data governance principles are implemented consistently and effectively, while providing continuous feedback based on delivery and operational experience to help evolve the architecture in response to real‑world usage, scalability, and reliability needs. The role owns the ingestion, transformation, and orchestration of data within Databricks; implements robust data quality, validation, incident response, and observability frameworks; and develops automated, reusable pipelines that reduce operational overhead and improve consistency. By strengthening the underlying data foundation through disciplined engineering practices, the position contributes directly to Tundra’s broader data and AI strategy by enabling mature, reliable, and sustainable data assets across the organization.

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related technical field
• 3–5+ years of experience in data engineering, data platform development, or a similar technical role involving software development, pipeline development, data integration, and cloud‑based data platforms
• Strong experience working with relational databases (SQL Server, Oracle, PostgreSQL, MySQL) and distributed data processing technologies
• Strong programming experience with Python, PySpark, and SQL
• Demonstrated understanding of software engineering principles, including modular design, testing, dependency management, and maintainable code patterns
• Strong understanding of the Databricks platform, including its core tools and data engineering features
• Experience working with cloud platforms, with preference for AWS (Databricks) and exposure to Azure
• Strong hands‑on experience building pipelines using Workflows, Delta Live Tables, notebooks, SQL, and Python
• Experience integrating structured and semi‑structured data from APIs, common file formats (JSON, CSV, Parquet), and object storage platforms
• Proficiency with Delta Lake best practices, scalable ETL/ELT design patterns, and modern data architecture principles
• Solid experience with Git-based workflows, CI/CD pipelines, and DevOps practices
• Experience using Terraform and Databricks Asset Bundles (DABs) to automate deployment of pipelines, alerts, and related assets
• Familiarity with data cataloging, governance practices, and enterprise data management standards
• Strong analytical and debugging skills with the ability to diagnose and resolve data pipeline and platform issues
• Understanding of Databricks connectivity to on‑prem and cloud data sources, including fundamental networking and secure access configurations

Nice To Haves

• Demonstrates strong engineering discipline, attention to detail, and the ability to manage multiple workloads, and priorities in a fast‑moving environment
• Applies analytical thinking to diagnose complex data issues, identify root causes, and implement effective solutions
• Collaborates effectively with Data Architect, data scientists, analysts, and business stakeholders to deliver high‑quality data products
• Clearly communicates technical concepts to both technical and non‑technical audiences, ensuring shared understanding and alignment
• Learns new tools, technologies, and patterns quickly; adapts to evolving platform capabilities and business needs
• Follows engineering best practices, contributes to standards, and consistently seeks opportunities to improve efficiency and reduce technical debt
• Understands stakeholder needs and delivers data solutions that are accurate, timely, and aligned with business priorities
• Produces clear, structured, and maintainable documentation that supports knowledge sharing, onboarding, and long‑term maintainability of data pipelines and platforms

Responsibilities

• Design, build, and maintain production‑grade data systems and pipelines within Databricks using Python, PySpark, SQL, Delta Live Tables, and Workflows
• Apply software engineering best practices—including modular design, code reuse, testing, versioning, and documentation—to data pipeline development
• Integrate data from relational databases (Oracle, SQL Server, MySQL, PostgreSQL), APIs, object storage, and semi‑structured sources (document databases, logs)
• Work with external data vendors to establish secure, reliable access to required datasets and ensure seamless integration into Databricks
• Implement and maintain data validation rules and observability practices—including lineage tracking, alerting, and performance monitoring—to ensure accuracy, completeness, and reliability across environments
• Contribute to metadata management and cataloging to support transparency and traceability across the data ecosystem
• Automate deployment and orchestration of data pipelines using Terraform and Databricks Asset Bundles (DABs)
• Design, maintain, and improve CI/CD pipelines that enforce code quality, testing, and controlled promotion of data assets across environments
• Apply structured incident management practices for data platforms and pipelines, including timely triage, impact assessment, root cause analysis (RCA), remediation, and contribution to post‑incident reviews to improve system reliability and prevent recurrence
• Work in close partnership with the Data Architect, translating architectural standards into production implementations and providing feedback based on operational and delivery experience
• Collaborate with business and analytics teams to understand data requirements and deliver reliable, production‑ready data assets
• Contribute to documentation, coding standards, and best practices that support a high‑quality engineering culture

Benefits

• Competitive total rewards, including salary, annual incentive pay, long‑term incentives for eligible roles, and employer‑matched RRSP contributions
• Comprehensive employer-paid benefits, including a $5,000 mental health benefit, Healthy Lifestyle Allowance, maternity top‑up, paid parental leave, and personal leave
• Ongoing learning and development opportunities, including training investments, leadership programs, professional designation reimbursement, engineer‑in‑training programs and company-wide learning opportunities

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level