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Data Engineer

5 months ago

Toronto, Canada SADA Full time

Join the Energy at SADA

Are you ready to be part of a team that's pushing the boundaries of data engineering? SADA is looking for a proficient Data Engineer to join our accomplished Data Engineering Team. This role offers both professional and personal growth by immersing you in a dynamic environment where you’ll collaborate with top-tier architects and engineers. You'll engage in recommending, prototyping, building, and debugging advanced data infrastructures on Google Cloud Platform (GCP), addressing real-world data challenges for a broad range of clients—from startups to industry pioneers. You'll blend technical skills with client interactions, crafting data strategies and witnessing the direct impact of your contributions.

As a valued team member, you will be recognized for your contributions, working independently with minimal oversight while playing an important role in multiple engagements. Seize this opportunity to grow professionally and personally as a key player by adding exceptional value through your deliverables. 

Responsibilities

Innovate and Solve Complex Problems: Design and build advanced software systems to efficiently process petabytes of data. Implement Best Practices: Quickly adapt and utilize the latest software engineering practices. Collaborate Effectively: Work alongside engineers, project managers, and solutions architects to deliver new products and features. Develop Data Pipelines: Design, implement, and maintain big data pipelines that drive critical business decisions. Maintain Quality: Ensure that all software you create is testable and thoroughly tested. Participate in Design Discussions: Actively contribute to architecture, design, and implementation discussions. Drive Project Success: Participate in planning, execution, and successful delivery of complex technical projects. Engage with Clients: Take part in technical discussions, translating technical concepts into clear, actionable strategies that align with their business objectives.

Pathway to Success

At SADA, we embrace change and innovation enthusiastically. Your success will be measured by the breadth, magnitude, and quality of your contributions. You will be part of a successful practice where your ability to estimate accurately, receive positive customer feedback, collaborate well with peers, and demonstrate consultative polish will be key to your growth.

As you continue to execute successfully, we will build a customized development plan together that leads you through the engineering or management growth tracks.

Expectations

Customer Interaction: Regular interaction with customers, including engineers, technical project managers, and executives. Training: On-going training with an initial orientation followed by a 90-day onboarding schedule. Required Travel: Minimal travel to customer sites, conferences, and related events.

Job Requirements

Required Credentials

Google Professional Data Engineer Certified or able to complete within the first 45 days of employment.

Required Qualifications

Expertise in at least one of the following domain areas:

Big Data: Managing Hadoop clusters, troubleshooting, migrating Hadoop workloads, architecting solutions, ETL pipelines with Spark, Apache Beam, messaging systems like Pub/Sub, Kafka. Data Warehousing: Building data warehouse solutions, schema designs, ETL/ELT pipelines, reporting tools. Data Migration: Migrating data stores to scalable cloud-based stores with minimal downtime. Backup and Disaster Recovery: Building large-scale production-grade data backup, restore, and disaster recovery solutions. Software Development: Experience writing software in Python, Java, Scala, or Go. Production-Grade Data Solutions: Experience with relational and NoSQL data solutions. System Monitoring and Performance Tuning: Experience in systems monitoring, capacity planning, and performance tuning. Technical Consulting: Experience in technical consulting or other customer-facing roles.

Preferred Qualifications

Google Cloud Data Products: Experience with CloudSQL, Spanner, Cloud Storage, Pub/Sub, Dataflow, Dataproc, Bigtable, BigQuery, Dataprep, Composer. IoT Architectures: Experience building real-time data streaming pipelines. Machine Learning: Applied experience operationalizing machine learning models on large datasets. Industry Trends: Knowledge of industry trends and new technologies, and the ability to apply these to architectural needs. Leadership and Self-Direction: Demonstrated leadership skills and the ability to teach others. Statistical Tools: Proficiency in selecting the right statistical tools for data analysis problems.