Intermediate Data Engineer | GCP | Hybrid

Position: Intermediate Data Engineer | GCP | Hybrid | 2-5 yrs
The Intermediate Data Engineer in the Wealth Data Engineering team, part of a global engineering organization, will be a key player in designing and implementing critical data solutions to meet the operational data needs of a large Wealth Management business. This role requires hands-on expertise in Big Data technologies, specifically Hadoop (Cloudera) and modern cloud data services like Google Cloud Platform (GCP), working collaboratively with enterprise data teams and solution architects to deliver end-to-end data solutions.

If you're interested, please apply this job posting with you updated resume or send your updated resume to , thank you.

Advantages
Compensation & Benefits:
Competitive rewards program, including a bonus, flexible vacation, personal days, sick days, and comprehensive benefits that start on day one.
Culture: A commitment to Diversity, Equity, Inclusion & Allyship, with a focus on creating an inclusive and accessible environment for all employees.
Professional Development:
Opportunities for upskilling through online courses, cross-functional development, and tuition assistance.
Work Environment: A dynamic ecosystem with opportunities for team collaboration.

Responsibilities
Data Pipeline Development:
Lead the development efforts for ingesting and transforming data from diverse sources. This includes hands-on coding, scripting, and specification writing to ensure end-to-end delivery of data into the Enterprise Data Lake environment.
Scalable Architecture:
Design, build, and operationalize distributed, reliable, and scalable data pipelines to ingest and process data from multiple sources.
Cloud Platform Expertise (GCP):
Utilize Google Cloud Platform (GCP) data services such as Data Proc, Dataflow, Cloud

SQL, Big Query, and Cloud Spanner, combined with technologies like Spark, Apache Beam/Composer, DBT, Confluent Kafka, and Cloud Functions.
Ingestion Patterns:
Design and implement versatile data ingestion patterns that support batch, streaming, and API interfaces for both data ingress and egress.
Technical Leadership:
Guide a team of data engineers, developing custom code and frameworks using best practices (Java, Python, Scala, Big Query, DBT, SQL) to meet demanding performance requirements.
Workflow Management:
Build and manage data pipelines with a deep understanding of workflow orchestration, task scheduling, and dependency management.
Technical Guidance:
Provide end-to-end technical expertise on effectively using cloud infrastructure to build solutions, creatively applying platform services to solve business problems, and communicating these approaches to various stakeholders.
Operational Excellence:
Provide guidance on implementing application logging, notification, job monitoring, and performance monitoring.

Qualifications

Experience:

2+ years of experience in data engineering, including performance optimization for large OLTP applications.
Big Data:
Strong knowledge of Hadoop concepts, including HDFS, Hive, Pig, Flume, and Sqoop.

Working experience in HQL.
Cloud Data Services (GCP):
Knowledge of primary managed data services within GCP, including Data Proc, Dataflow (Java/Python for streaming/batch jobs), Big Query/DBT, Cloud Spanner, and Cloud Pub/Sub.
Databases & Streaming:
Knowledge of Google Cloud Platform Databases (SQL, Spanner, Postgre

SQL), relational/No

SQL databases, and data streaming technologies such as Kafka and Spark-streaming.
Software Development:
Knowledge of Java microservices and Spring Boot. Working knowledge of developing and scaling JAVA REST services using frameworks like Spring.
Architecture & Operations:
Strong architecture knowledge with experience in providing technical solutions for cloud infrastructure. Knowledge of Infrastructure as Code (IaC) practices and frameworks like Terraform.

Soft Skills:

Good communication and problem-solving skills with the ability to effectively convey ideas to business and technical teams.
Nice-To-Have:
Understanding of the Wealth business line and the data domains required for building end-to-end solutions.

Summary
The Intermediate Data Engineer in the Wealth Data Engineering team, part of a global engineering organization, will be a key player in designing and implementing critical data solutions to meet the operational data needs of a large Wealth Management business. This role requires hands-on expertise in Big Data technologies, specifically Hadoop (Cloudera) and modern cloud data services like Google Cloud Platform (GCP), working collaboratively with enterprise data teams and solution architects to deliver end-to-end data solutions.

If you're interested, please apply this job posting with you updated resume or send your updated resume to , thank you.

Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout…