In-Demand Snowflake data engineer Jobs

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Wenco (a Hitachi Construction Machinery subsidiary) —Eight Mile Plains QLD
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11d
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Capgemini —Newcastle NSW
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NCS Australia —Macquarie Park NSW
- Full-time
- Hybrid work
- Data analysis skills
- SQL
- AWS
- Parental leave
Quick Apply

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Data Engineer

Capgemini -
Newcastle NSW

Apply Now

Job Details

Qualifications

Data modelling
Azure
Data lake
Azure Data Lake
SAS
AWS Certification
Google Cloud Platform
AWS
Analysis skills
Data pipeline
S3
Redshift
DynamoDB
AI
RDS database
Communication skills
Data warehouse

Full Job Description

Who You Will Be Working With

The Data Platforms team is part of the Insights and Data Global Practice and has seen strong growth and continued success across a variety of projects and sectors. Data Platforms is the home of the Data Engineers, Platform Engineers, Solutions Architects and Business Analysts who are focused on driving our customers digital and data transformation journey using the modern cloud platforms.

We specialise on using the latest frameworks, reference architectures and technologies using AWS, Azure and GCP along with various data platforms like Databricks, Snowflake, Quantexa, Palantir, SAS.

The Role You Are Considering

As a Data Engineer, you will be an integral part of our team dedicated to building scalable and secure data platforms. You will leverage your expertise to design, develop, and implement data warehouses, data lakehouses, and AI/ML models that fuel our data-driven operations.

What You Will Bring

Design and build high-performance data pipelines: to extract, transform, and load data into Cloud Data Lake Storage and other Cloud services.
Develop and maintain secure data warehouses and data lakehouses: Implement data models, data quality checks, and governance practices to ensure reliable and accurate data.
Implement ETL/ELT Processes: Develop Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) workflows to seamlessly move data from source systems to Data
Warehouses, Data Lakes, and Lake Houses using Open Source and cloud tools.

Build and deploy AI/ML models: Integrate Machine Learning into data pipelines, leveraging ML to develop predictive models and drive business insights.
Monitor and optimize data pipelines and infrastructure: Analyze performance metrics, identify bottlenecks, and implement optimizations for efficiency and scalability.
Collaborate with cross-functional teams: Work closely with business analysts, data scientists, and DevOps engineers to ensure successful data platform implementations.
Stay ahead of the curve: Continuously learn and adapt to the evolving landscape of big data technologies and best practices with a focus on how AI can support you in your delivery work
Minimum 10+ years of experience as a Data Engineer or similar role.
Proven expertise in the technologies below, and data pipeline development and strong understanding of data warehousing concepts and practices.
Excellent problem-solving and analytical skills and strong communication and teamwork skills.

In addition to these core skills, you should have specialist experience in one or more of the following technologies

Azure Databricks

Design and build high-performance data pipelines: Utilize Databricks and Apache Spark to extract, transform, and load data into Azure Data Lake Storage and other Azure services.
Experience of Databricks ML and Azure ML to develop predictive models and drive business insights.
Proven expertise in Databricks, Apache Spark, and data pipeline development and strong understanding of data warehousing concepts and practices.
Experience with Microsoft Azure cloud platform, including Azure Data Lake Storage, Databricks and Azure Data Factory.
Azure Data Engineer Associate and Databricks Certified Data Engineer Professional

AWS

Proficiency with AWS Tools: Demonstrable experience using AWS Glue, AWS Lambda, Amazon Kinesis, Amazon EMR , Amazon Athena, Amazon DynamoDB, Amazon Cloudwatch, Amazon SNS and AWS Step Functions.
Programming Skills: Strong experience with modern programming languages such as Python, Java, Scala & Pyspark.
Expertise in Data Storage Technologies: In-depth knowledge of Data Warehouse, Database technologies, and Big Data Eco-system technologies such as AWS Redshift, AWS RDS, and Hadoop.
Experience with AWS Data Lakes: Proven experience working with AWS data lakes on AWS S3 to store and process both structured and unstructured data sets

Further Info

Security Clearance: To be successfully appointed to this role, must be eligible to obtain Security Check (SC)clearance.

To obtain SC clearance, the successful applicant must have resided continuously within the United Kingdom for the last 5 years, along with other criteria and requirements.

Throughout the recruitment process, you will be asked questions about your security clearance eligibility such as, but not limited to, country of residence and nationality.
Some posts are restricted to sole UK Nationals for security reasons; therefore, you may be asked about your citizenship in the application process.

Hybrid working: The places that you work from day to day will vary according to your role, your needs, and those of the business; it will be a blend of Company offices, client sites, and your home; noting that you will be unable to work at home 100% of the time.

If you are successfully offered this position, you will go through a series of pre-employment checks, including: identity, nationality (single or dual) or immigration status, employment history going back 3 continuous years, and unspent criminal record check (known as Disclosure and Barring Service)

What we’ll offer you

You will be encouraged to have a positive work-life balance. Our hybrid-first way of working means we embed hybrid working in all that we do and make flexible working arrangements the day-to-day reality for our people. All UK employees are eligible to request flexible working arrangements.

You will be empowered to explore, innovate, and progress. You will benefit from Capgemini’s ‘learning for life’ mindset, meaning you will have countless training and development opportunities from thinktanks to hackathons, and access to 250,000 courses with numerous external certifications from AWS, Microsoft, Harvard Manage Mentor, Cybersecurity qualifications and much more.

Why we’re different

At Capgemini, we help organisations across the world become more agile, more competitive, and more successful. Smart, tailored, often ground-breaking technical solutions to complex problems are the norm. But so, too, is a culture that’s as collaborative as it is forward thinking. Working closely with each other, and with our clients, we get under the skin of businesses and to the heart of their goals. You will too.

Capgemini is proud to represent nearly 130 nationalities and its cultural diversity. Our holistic definition of diversity extends beyond gender, gender identity, sexual orientation, disability, ethnicity, race, age, and religion. Capgemini views diversity as everything that makes us who we are as an organization, including our social background, our experiences in life and work, our communication styles and even our personality. These dimensions contribute to the type of diversity we value the most: diversity of thought.

Apply Now

Refine Your Search

snowflake data engineer jobs

Data Engineer

AWS API Engineer / Data Architect

Senior Technical Support Engineer, Observe by Snowflake

Databricks Data Solution Architect

Planning Analytics Engineer

Analytics Engineer

Python Developer

Azure Data Solution Architect

Software Engineer â€“ Data & AI Platforms - Financial Markets

Data Engineer (HCM)

Data Consultant, Melbourne

Consultant - Data & Analytics

Senior Data Engineer -Coalesce

AI & Data Architect (Enterprise AI & Data Platforms)

Senior Data Engineer - Data Platform

Machine Learning Engineer - Capital Investment - Data & AI - Sydney

AWS Data Solution Architect

Senior Manager, Data Analytics Consulting

AI Engineer

Technical Data Business Analyst (EXPRESSIONS OF INTEREST)

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