Akkodis is seeking a Data Engineer for a contract position with a client in GTA and is ideally looking for experienced Azure data bricks, Hadoop, Spark, Kafka
Position : Data Engineer
Location : GTA
Contract
What You’ll Do :
- 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.
- Write production-ready, testable code that adheres to engineering best practices and accounts for edge cases and error handling.
- Ensure high-quality deliverables through code reviews, design reviews, and adherence to architectural guidelines.
- 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.
- Provide technical guidance and mentorship to junior engineers on the team.
- Collaborate with data scientists, 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.
What You’ll Bring :
3+ years of experience in a data engineering role, with a focus on building scalable, reliable, and high-performance data systems.Hands-on experience in building or architecting data engineering solutions for commercial data and analyticsHands-on experience on one or more of the following tech – Azure databricks (must have), Python, Tableau (good to have) and experience implementing commercial data warehouse through Medallion architectureUnderstanding and strong experience in Pharma / LSHCHands on experience in building or architecture data engineering solutions for commercial data and analyticsExtensive 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).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.Strong communication skillsAdditional Skills :
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.Strong understanding of data modeling, data warehousing, and data lake concepts and best practices