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Sr Data Engineer information
What is a Sr data engineer?
Sr Data Engineers, or Senior Data Engineers, are experienced professionals responsible for designing, building, and maintaining scalable data pipelines and architectures. They work with large datasets, ensuring data quality, reliability, and accessibility for analytics and business intelligence purposes. Sr Data Engineers collaborate with data scientists, analysts, and other stakeholders to implement data solutions that support decision-making and business growth. Their expertise often includes proficiency in programming languages like Python or Java, experience with big data tools such as Hadoop or Spark, and a deep understanding of database systems.
How do Sr data engineers typically collaborate with data scientists and analysts within a project team?
Sr Data Engineers play a crucial role in bridging the gap between raw data and actionable insights. They work closely with data scientists and analysts to understand data requirements, design robust data pipelines, and ensure the reliability and scalability of data infrastructure. Regular collaboration involves translating analytical needs into technical specifications, optimizing data flow, and troubleshooting data issues. This teamwork ensures that data-driven projects progress smoothly and that the analytical team has timely access to clean, well-structured data.
What are the key skills and qualifications needed to thrive as a Sr data engineer, and why are they important?
To thrive as a Sr Data Engineer, you need expertise in data architecture, ETL processes, programming (such as Python or Scala), and a strong background in computer science or a related field. Familiarity with big data technologies like Hadoop, Spark, cloud platforms (AWS, Azure, GCP), and database management systems, along with relevant certifications, is typically required. Advanced problem-solving abilities, attention to detail, and strong collaboration skills help set top performers apart in this role. These skills and qualities ensure the efficient design, implementation, and maintenance of robust data pipelines that enable data-driven decision-making across the organization.
What is the difference between Sr Data Engineer vs Data Engineer?
| Aspect | Sr Data Engineer | Data Engineer |
|---|---|---|
| Required Credentials | Bachelor's degree in CS or related field; 3+ years experience; SQL, Python, Spark | Bachelor's degree in CS or related field; 1-3 years experience; SQL, Python, Spark |
| Work Environment | Collaborates with data scientists, analysts; designs scalable data pipelines | Builds and maintains data pipelines; supports data analysis |
| Employer & Industry Usage | Tech companies, finance, healthcare; used for complex data projects | Startups, enterprises; used for data collection and processing |
The main difference between a Sr Data Engineer and a Data Engineer lies in experience level, responsibilities, and complexity of projects. Sr Data Engineers typically have more experience, handle more complex data architecture, and mentor junior staff, whereas Data Engineers focus on building and maintaining data pipelines. Both roles are essential in data-driven organizations, but the senior role involves greater technical leadership and strategic planning.
What are popular job titles related to Sr Data Engineer jobs in Alberta?
For Sr Data Engineer jobs in Alberta, the most frequently searched job titles are:
What cities in Alberta are hiring for Sr Data Engineer jobs?
Cities in Alberta with the most Sr Data Engineer job openings:
Full-time
Re-posted 5 days ago
Job description
Senior Azure Databricks Engineer (Contract)
Location: Calgary, AB (Onsite 4 days/week – downtown)
Term: 3–6 months
Engagement: Contract through a consulting firm delivering data engineering projects for multiple enterprise clients Industry: Oil & Gas (experience a plus, not required)
A leading Canadian consulting firm is seeking an experienced Databricks Engineer to support several high-impact client initiatives. This is a hands-on engineering role focused on designing, building, and optimizing data workloads on Azure Databricks, including large-scale migrations, platform modernization, and AI/ML enablement.
You will work directly with consulting delivery teams and enterprise client stakeholders in a fast-moving, collaborative environment. This role requires Calgary-based candidates who are comfortable being onsite 4 days per week.
Key Responsibilities
- Design and develop Azure Databricks solutions using PySpark and SQL
- Build and optimize batch and streaming data pipelines
- Migrate legacy workloads from Synapse and other platforms into Databricks
- Implement Delta Lake patterns (medallion architecture, CDC, data quality)
- Integrate Databricks with Azure services such as ADLS, ADF, Azure Key Vault, and Azure DevOps/GitHub
- Optimize performance and cost (cluster sizing, job orchestration, query tuning)
- Collaborate with architects, analytics engineers, and client teams
- Contribute to reusable accelerators, internal standards, and best practices
- Support client enablement through documentation and knowledge transfer
- Implement governance models and Unity Catalog (access controls, lineage, security frameworks)
Required Skills & Experience
- 7+ years of data engineering experience
- 4+ years of recent, hands-on Databricks engineering (Delta Lake, Unity Catalog, MLflow)
- Strong experience building production pipelines using PySpark and SQL
- Proven experience with Azure data services: ADLS, ADF, Synapse, Key Vault, Azure DevOps
- Experience leading large-scale data migrations and ETL/ELT workload modernization
- Strong understanding of lakehouse architecture, medallion patterns, and modern ELT/ETL approaches
- Experience implementing CI/CD for data workloads
- Strong understanding of enterprise data governance, security, and compliance
- Excellent communication skills with comfort working directly with client teams
- Previous consulting or professional services experience strongly preferred
- Databricks and Azure certifications considered an asset
Ideal Candidate Profile
A strong, hands-on Azure Databricks engineer who:
- Delivers high-quality, production-ready code
- Understands modern data architecture and platform design
- Communicates clearly with technical and non-technical stakeholders
- Thrives in collaborative, client-facing environments
- Is comfortable being onsite 4 days/week
- Can contribute immediately to high-impact migration and modernization projects
Why This Role Stands Out
- Contract through a delivery-focused consulting firm supporting multiple enterprise clients
- Work on high-impact Oil & Gas data initiatives (migration, modernization, AI/ML enablement)
- Exposure to advanced Databricks features and architecture patterns
Only qualified candidates will be contacted for next steps. Thank you in advance for your interest.