Lead Full Stack Engineer

Scotiabank • Toronto, ON

1d • Onsite

About The Position

The Wealth Data engineering team within the Global Wealth Engineering (GWE) is the key team in meeting the operational data needs of the various stake holders within Wealth Management. The Lead Data Engineer will play a key role in designing and implementing data solutions using Big Data, Hadoop (Cloudera) and Google cloud working closely with the enterprise data team and data architects, solution architects, business systems analyst and data engineers.

Requirements

• 10+ years of experience in data engineering, performance optimization for large OLTP applications
• Expertise knowledge of Hadoop HDFS, Hive, Pig, Flume and Sqoop.
• Experience with the primary managed data services within GCP, including DataProc, Dataflow, BigQuery/DBT, Cloud Spanner, Cloud SQL, Cloud Pub/Sub etc.
• Experience with Google Cloud Platform Databases (SQL, Spanner, PostgreSQL)
• Working experience in HQL
• Good knowledge of the concepts of Hadoop.
• Experience working with relational/NoSQL databases
• Experience with data streaming and technologies such as Kafka, Spark-streaming etc.
• Experience with Infrastructure as Code (IaC) practices and frameworks like Terraform
• Knowledge of Java microservices and Spring Boot
• Strong architecture knowledge with experience in providing technical solutions for cloud infrastructure.
• Working knowledge of developing and scaling JAVA REST services, using frameworks such as Spring
• Good communication and problem-solving skills. Ability to effectively convey ideas to business and technical teams

Nice To Haves

• Understanding of Wealth business line and the various data domains required for building an end to end solution

Responsibilities

• Leading development efforts in ingesting and transforming data from various sources.
• Working in the Big Data/ Hadoop environment, should be hands on in writing code, building scripts, writing specifications and responsible for end to end delivery of data in the Enterprise Data Lake environment
• Build distributed, reliable and scalable data pipelines to ingest and process data from multiple data sources
• Designing, building, operationalizing the data platform using Google Cloud Platform (GCP) data services such as DataProc, Dataflow, CloudSQL, BigQuery, CloudSpanner in combination with third parties such as Spark, Apache Beam/ composer, DBT, Cloud PubSub, Confluent Kafka, Cloud storage Cloud Functions & Github
• Designing and implementing data ingestion patterns that will support batch, streaming and API interface on both the Ingress and Egress.
• Guide a team of data engineers and work hands on in developing framework and custom code using best practices that will meet the demanding performance requirements
• Take a lead in designing and building production data pipelines from data ingestion to consumption using GCP services, Java, Python, Scala, BigQuery, DBT, SQL etc.
• Experience using Cloud Dataflow using Java/Python for deploying streaming jobs in GCP as well as batch jobs using text/JSON files and writing them to BigQuery
• Building and managing data pipelines with a deep understanding of workflow orchestration, task scheduling and dependency management
• Provide end-to-end technical guidance and expertise on how to effectively use Google Cloud to build solutions; creatively applying cloud infrastructure and platform services to help solve business problems; and communicating these approaches to different business users
• Provide guidance on Implementing application logging, notification, jobs monitoring and performance monitoring

Benefits

• Upskilling through online courses, cross-functional development opportunities, and tuition assistance.
• Competitive Rewards program including bonus, flexible vacation, personal, sick days and benefits will start on day one.
• Free tea & coffee, universal washrooms, and lots of space for team collaboration.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed