In-Demand Snowflake data engineer Jobs

Data Scientist
Accenture —Melbourne VIC
- Full-time
- Azure
- SQL
- Machine learning
- On-the-job training
- Parental leave
9d
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- Full-time
- Hybrid work
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Automation Engineer – Data Platform
Duxxel Pty Ltd —Sydney NSW
- From $120,000 a year
- Full-time
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Senior Data Engineer - AWS(Kinesis/Lambda/MSK/Flink)
Capgemini —Melbourne VIC
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Blinq —Melbourne VIC
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Principal Data Engineer
REA Group —Richmond VIC
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Data Engineering Lead
Infosys —Melbourne VIC
- $129,311 - $142,018 a year
- Azure
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10h
Data Engineering Manager
The GPT Group —Sydney NSW
- Full-time
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Staff Data Engineer (Data Platform & Modernisation)
Commonwealth Bank —Sydney NSW
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Snowflake —Melbourne VIC
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Deloitte —Sydney NSW
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Director Technology - Data Platforms
Simple Machines —Sydney NSW
- Hybrid work
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Ippon Technologies —Melbourne VIC
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Varonis —Sydney NSW
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Gigacomm —Brisbane QLD
- Full-time
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Synogize —Sydney NSW
- Full-time
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Quick Apply
Senior Customer Engineer - ANZ
Mixpanel —Sydney NSW
- Contract
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- Health insurance
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Practiv —Sydney NSW
- Full-time
- Hybrid work
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Data Scientist

Accenture -
Melbourne VIC

Apply Now

Job Details

Full-time
9 days ago

Benefits

On-the-job training
Parental leave

Qualifications

Pricing
Power BI
Azure
Computer vision
Supply chain
NumPy
Git
SQL
Machine learning
Agile
AI
Communication skills
Analytics

Full Job Description

The Data Scientist is responsible for developing analytical and machine-learning solutions that support decision-making across customer, commercial, supply chain, store operations, loss and corporate domains.

The role works with business stakeholders, product teams, data engineers and AI/ML engineers to translate business problems into measurable analytical outcomes. It covers data exploration, statistical analysis, feature development, predictive modelling, experimentation, model evaluation and ongoing performance monitoring.

The role is expected to deliver practical, explainable and production-ready analytical solutions rather than standalone research or proof-of-concept models.

Key Responsibilities

Data Science and Modelling

Translate business problems into clearly defined analytical hypotheses and modelling approaches.
Explore and analyse large structured and semi-structured datasets.
Develop predictive, classification, forecasting, optimisation and segmentation models.
Undertake feature selection, feature engineering and model experimentation.
Evaluate models using appropriate statistical and commercial performance measures.
Compare model approaches and document assumptions, limitations and trade-offs.
Develop interpretable outputs that support business decision-making.
Contribute to recommendation, pricing, promotion, demand, customer and operational analytics use cases.

Experimentation and Measurement

Design and analyse experiments, including A/B tests and controlled trials.
Establish appropriate control groups, success measures and evaluation criteria.
Assess model and initiative performance against agreed business outcomes.
Support causal analysis, incrementality measurement and scenario modelling.
Communicate statistical confidence, uncertainty and limitations clearly.

Productionisation and Model Lifecycle

Work with AI/ML engineers and data engineers to productionise models.
Develop reusable and maintainable Python and SQL code.
Support model deployment, validation, monitoring and retraining processes.
Monitor model accuracy, drift, bias and business performance.
Maintain model documentation, feature definitions and evaluation evidence.
Contribute to model governance, approval and risk-management activities.
Investigate model-performance issues and recommend corrective actions.

Stakeholder Collaboration

Work with product owners and business stakeholders to define analytical requirements.
Explain complex modelling outcomes in clear business language.
Present insights, recommendations and commercial implications.
Work within cross-functional product and engineering teams.
Support prioritisation of analytical opportunities based on value, feasibility and data readiness.

Required Skills and Experience

Experience delivering data-science or advanced-analytics solutions in an enterprise environment.
Experience with Snowflake, Databricks or comparable cloud data platforms.
Experience with Azure-based data and machine-learning services.
Strong Python skills, including experience with pandas, NumPy, scikit-learn or equivalent libraries.
Strong SQL capability and experience working with large analytical datasets.
Practical knowledge of statistical modelling, machine learning and experimental design.
Experience developing models such as:
- Classification and regression
- Time-series forecasting
- Segmentation and clustering
- Recommendation or propensity models
- Optimisation or scenario models
Experience with data preparation, feature engineering and model evaluation.
Ability to translate analytical outputs into business recommendations.
Experience using Git and collaborative software-development practices.
Strong communication, documentation and stakeholder-management skills.
Tertiary qualification in data science, statistics, mathematics, computer science, engineering, econometrics or a related discipline

Desirable Skills

Exposure to MLflow, feature stores, model registries or MLOps practices.
Experience with Power BI, MicroStrategy or comparable visualisation platforms.
Knowledge of retail, customer, commercial, pricing, promotion, supply chain or store operations.
Experience with causal inference, optimisation, operations research or econometrics.
Exposure to generative AI, natural-language processing or computer vision.
Experience working in Agile product teams.

Key Deliverables and Success Measures

Accurate, explainable and commercially relevant analytical models.
Measurable improvement in agreed business or operational outcomes.
Models successfully transitioned into production and operational use.
Robust experimentation and model-evaluation evidence.
Effective monitoring of model accuracy, drift and business performance.
Reusable code, documented assumptions and traceable analytical outputs.
Positive engagement with product, engineering and business stakeholders.

Benefits of working at Accenture:

18 weeks paid parental leave
Long & short-term career break opportunities
Structured career development program
Local and international career opportunities.
Certified as a Family Inclusive Workplace™
Flexible Work Arrangements - centered around Accenture’s Truly Human ethos and our commitment to supporting the health and wellbeing of our people.
We are proud to be in the top 3 of last year’s Diversity & Inclusion Index!

We are a WORK180 Endorsed Employer, to see our benefits and policies click here

All our consulting professionals receive comprehensive training covering business acumen, technical and professional skills development. You’ll also have opportunities to hone your functional skills and expertise in an area of specialization. We offer a variety of formal and informal training programs at every level to help you acquire and build specialized skills faster. Learning takes place both on the job and through formal training conducted online, in the classroom, or in collaboration with teammates. The sheer variety of work we do, and the experience it offers, provide an unbeatable platform from which to build a career.

Accenture is a an EEO and Affirmative Action Employee of Females/Minorities/Veterans/Individuals with Disabilities.

Equal Employment Opportunity Statement for Australia:

At Accenture, we recognise that our people are multi-dimensional, and we create a work environment where all people feel like they can bring their authentic selves to work, every day.

Our unwavering commitment to inclusion and diversity unleashes innovation and creates a culture where everyone feels they have equal opportunity. Our range of progressive policies support flexibility in ‘where’, ‘when’ and ‘how’ our people work to ensure that Accenture is an organisation where you can strive for more, achieve great things and maintain the balance and wellbeing you need.

We encourage applications from all people, and we are committed to removing barriers to the recruitment process and employee lifecycle. All employment decisions shall be made without regard to age, disability status, ethnicity, gender, gender identity or expression, religion or sexual orientation and we do not tolerate discrimination. If you require adjustments to the recruitment process or have a preferred communication method, please email [email protected] and cite the relevant Job Number, or contact us on +61 2 9005 5000.

To ensure our workplace is inclusive and diverse we are setting bold goals and taking comprehensive action. To achieve these goals, we collect information that allows us to track the effectiveness of our Inclusion and Diversity programs. Learn how Accenture protects your personal data and know your rights in relation to your personal data. Read more about our Privacy Statement.

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Apply Now

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snowflake data engineer jobs

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