Data Engineer

3 weeks ago

Melbourne, Victoria, Australia Deputy Full time

This is a Data Engineer - Analytics role with one of the leading companies in AU right now. They are continuing to grow rapidly. This is the chance to join right as the company takes off.

More About the Role at Deputy

At Deputy, we empower businesses to build thriving workplaces - ones where staff are engaged, customers are served well, businesses are legally compliant, and companies' profits thrive. Our reach extends across 100+ countries worldwide, serving more than 330,000 workplaces. Deputy is a global SaaS workforce management company headquartered in Sydney, San Francisco, and London, backed by top investors and recently surpassed $100m in annual recurring revenue. We've helped millions of workers across industries and aim to empower 80% of the global workforce. If you're passionate about improving the world of work, one shift a time, join us at Deputy and help shape the future of hourly employment.

As our dedicated Data Engineer - Analytics, you will work closely with both Data Engineering and Analytics teams to enhance our data and analytics ecosystem. With a focus on scalable design, you will be instrumental in building and optimizing the key components of our data infrastructure to support company growth.

The Role

This role bridges analytics and data engineering, bringing software engineering expertise to a team of business-focused analysts and data scientists. You will be responsible for productionising data applications, such as forecasting and attribution models, and expanding our architecture for the data warehouse, BI tools, and machine learning use cases. Additionally, you'll streamline data flows to support cross-functional teams.

You

You are a skilled engineer who enjoys working on data systems, building them from scratch, and optimizing existing ones. As a Data Engineer - Analytics, you will collaborate with software developers, data architects, analysts, and data scientists on data-driven projects. You are comfortable supporting various teams, systems, and products, and motivated to improve or re-design our company's data infrastructure to enable the next generation of products and analytics initiatives.

Responsibilities

  1. Design and optimize data pipelines and architectures using Databricks Data Warehouse to ensure scalability and reliability for large data volumes.
  2. Collaborate with Data Engineering to build infrastructure for data extraction, transformation, and loading using SQL and AWS big data technologies.
  3. Utilize dbt to create and maintain efficient data models, transforming raw data into actionable insights for key stakeholders.
  4. Develop advanced analytics solutions, including machine learning models and predictive analytics, to support data-driven decision-making.
  5. Work closely with cross-functional teams, including Data Scientists, Engineers, and Product Managers, to deliver data solutions that align with business objectives.
  6. Establish best practices for data quality, governance, and security to maintain the integrity of our data assets.
  7. Provide mentorship and share expertise on analytics and data engineering best practices with team members.
  8. Stay informed on emerging technologies and techniques in data engineering and analytics to drive innovation.
  9. Advocate for data-driven decision-making by promoting the value of data across teams and fostering collaboration.

Skills & Experience

  • 2-3 years of experience in a technical analytics or data engineering role, with a strong background in Computer Science, Information Systems, or a related field.
  • Expertise in SQL and relational databases, with experience working on data modeling and query authoring.
  • Skilled in using dbt for data modeling and transformation, with a focus on building efficient data pipelines.
  • Experience in applying advanced analytics techniques, such as machine learning and predictive modeling, to real-world challenges.
  • Proficiency in using analytical tools and libraries like Pandas, NumPy, and Scikit-learn for data analysis and model development.
  • Working knowledge of stream processing and scalable data stores.
  • Solid understanding of data management principles, governance best practices, and regulatory requirements.
  • Ability to communicate technical insights to non-technical stakeholders, driving alignment on data initiatives.
  • Familiarity with below preferred software/tools:

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