Associate Engineer - Data

at  ZS Associates

• Toronto, Canada
• Canada
• Technology
• 20420
  ZS is a place where passion changes lives. As a management consulting and technology firm focused on transforming global healthcare and beyond, our most valuable asset is our people. Here you’ll work side-by-side with a powerful collective of thinkers and experts shaping life changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client first mentality to each and every engagement. We partner collaboratively with our clients to develop products that create value and deliver company results across critical areas of their business including portfolio strategy, customer insights, research and development, operational and technology transformation, marketing strategy and many more. Bring your curiosity for learning; bold ideas; courage and passion to drive life changing, impact to ZS.

TECHNICAL SKILLS:

• Proficiency in programming languages such as Python, Java, or Scala.
• Extensive experience with big data technologies (e.g., Hadoop, Spark, Kafka) and cloud-based data platforms (e.g., AWS, Azure).
• Expertise in data integration and ETL tools (e.g., Talend, Informatica, Apache NiFi).
• Strong understanding of data warehousing, and data lake concepts and best practices.
• Familiarity with CI/CD pipelines and application monitoring practices.
• Certifications in data engineering (e.g., Azure Data Engineer, AWS Certified Data Engineer) are a plus.

EXPERIENCE:

• 1-3 years of experience in a data engineering role, with a focus on building scalable, reliable, and high-performance data systems.
• Proven track record of designing and implementing data pipelines, data storage solutions, and data processing workflows.
• Hands-on experience with distributed computing frameworks and cloud-based data services.
• Demonstrated ability to collaborate with cross-functional teams and communicate technical solutions effectively.
• Ability to travel to other client offices or locations as needed.
• Strong communications skills.

Data Engineering:

• Build, Orchestrate and optimize data pipelines to extract, transform, and load (ETL) data from various sources into the organization’s data warehouse or data lake.
• Integrate data from diverse sources such as databases, APIs, streaming platforms, and file systems into cohesive data pipelines
• Implement data integration solutions that support real-time, batch, and incremental data processing.
• Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of data.
• Develop and maintain high-performance data pipelines that seamlessly integrate data from various sources into data warehouses, data lakes, and other storage solutions.
• Develop monitoring and alerting mechanisms to identify and address data quality issues proactively.
• Optimize ETL (Extract, Transform, Load) processes for efficiency, reliability, and data quality.
• Implement and manage data storage solutions, including relational databases, NoSQL databases, and distributed file systems.
• Manage the infrastructure and resources required to support data engineering workflows, including compute clusters, storage systems, and data processing frameworks.
• Implement security controls and data governance measures for an application to protect sensitive data and ensure compliance with regulatory requirements such as GDPR, CCPA, HIPAA, and PCI-DSS. Implement encryption, access controls, and auditing mechanisms to safeguard data privacy and integrity.

Data Engineering Best Practices:

• Write production-ready, testable code that adheres to engineering best practices and accounts for edge cases and error handling.
• Develop comprehensive unit tests and integration tests to validate data pipeline functionality and data integrity.
• Stay up to date with the latest data engineering tools, technologies, and methodologies, and evaluate their applicability to the team’s needs.

Technical Collaboration:

• Collaborate with business analysts, and other stakeholders to understand data requirements and translate them into robust engineering solutions.
• Work closely with other engineering teams to integrate data solutions seamlessly into the overall technology ecosystem.
• Participate actively in agile ceremonies, communicate progress, and manage dependencies effectively.

Education / Skills / Experience Education: A relevant bachelor’s or master’s degree in computer science, Data Engineering, or a related technical field.