Data Analysis Engineer

Reposted 14 Days Ago
Be an Early Applicant
Sydney, New South Wales
In-Office
Senior level
Artificial Intelligence • Information Technology
The Role
The Data Analysis Engineer analyzes banking datasets, validates data quality, creates Python scripts, and collaborates with teams to enhance data-driven decision making.
Summary Generated by Built In

About RDC

Rich Data Co (RDC) Delivering the Future of Credit, Today! We believe credit should be accessible, fair, inclusive, and sustainable. We are passionate about AI and developing new techniques that leverage traditional and non-traditional data to get the right decision in a clear and explainable way. Global leading financial institutions are leveraging RDC’s AI decisioning platform to offer credit in a way that aligns to the customers’ needs and expectations. RDC uses explainable AI to provide banks with deeper insight into borrower behaviour, enabling more accurate and efficient lending decisions to businesses.

Purpose of Role

The Data Analyst Engineer is responsible for performing detailed data exploration, validation, and mining to support strategic initiatives and project delivery across RDC’s banking and financial products. This role requires strong proficiency in SQL and Python, along with hands-on experience in banking data domains, to enable data-driven problem solving, data quality assessment, and analytical modelling.



Accountability & Outcomes

Support RDC project teams with end-to-end data analysis, including ingestion validation, data profiling, and mining. Conduct deep dives into structured and semi-structured banking datasets to identify issues, uncover insights, and detect patterns. Develop Python-based data exploration scripts, profiling tools, and reusable components to accelerate analysis. Write advanced SQL queries to support data profiling, joining, and reconciliation. Validate business logic and pipeline outputs through code-level data checks and cross-source reconciliation. Collaborate with Data Engineers, Business Analysts, and Product Owners to clarify data definitions and define the scope of analysis. Contribute to data quality frameworks, documentation, and ongoing improvements in data assurance processes. 



Capabilities

Experience

Essential

Minimum 5+ years of experience in a data analysis, data exploration, or data science role. Proven experience working with Python for data analysis, including pandas, numpy, and data validation libraries. Strong experience with SQL for complex querying, joining, filtering, and aggregating large datasets. Demonstrated experience working with banking or financial services datasets (e.g. credit risk, transactions, lending, customer profiles). Hands-on experience performing detailed data quality checks, root cause analysis, and reconciliation across large datasets. Experience supporting data delivery for analytics, ML models, or data pipeline validation. 

Desirable

Experience supporting machine learning or decisioning models from a data perspective. Experience working in an Agile delivery environment or using JIRA/Confluence for tracking. Experience performing cross-environment data checks. Experience with CI/CD pipelines or Git-based workflows for Python analysis code. Familiarity with data warehouse solutions and BI reporting layers. 



Knowledge and Skill

Essential

High proficiency in SQL and Python for hands-on analysis and scripting. Strong understanding of data exploration techniques, feature validation, and anomaly detection. Good knowledge of SQL for joining, filtering, and validating relational data. Familiarity with banking and finance data structures such as account transactions, balances, credit history, or compliance datasets. Ability to clearly document and communicate technical analysis to data and product teams. Comfortable working with JSON/Parquet formats, cloud file stores, and data lineage tracking. Ability to identify trends, outliers, and edge cases through analysis and communicate findings effectively. Strong documentation and verbal communication skills to share findings with both technical and non-technical stakeholders.  



Desirable

Understanding of data governance principles, data lineage, and metadata standards. Experience working with Spark, Airflow, or PySpark for distributed analysis. Awareness of data privacy and compliance standards. Knowledge of cloud data ecosystems. Use of Git, CI/CD pipelines, or script packaging for reusability and team collaboration. 





Join the Future of Credit!

  • Work at a 5-Star Employer of Choice 2023 - RDC was named one of HRD Australia’s “best companies to work for in Australia”.
  • Join a fast-growing global AI company - Grow your skills, capabilities and gain AI and global experience.
  • High performance team - Work alongside some of the best product teams, data scientists and credit experts in the industry.
  • Vibrant team culture - Join an innovative and agile team who celebrates wins and solves problems together.
  • Work-life balance - Highly flexible working arrangements - work how’s right for you!
  • Financial inclusion - Be part of a company that is striving for global financial inclusion and driving the future of credit.

Top Skills

Airflow
Ci/Cd
Confluence
Git
JIRA
JSON
Numpy
Pandas
Parquet
Pyspark
Python
Spark
SQL
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The Company
HQ: North Sydney, New South Wales
75 Employees
Year Founded: 2016

What We Do

RDC.AI (previously Rich Data Co) helps banks unlock deeper customer insight, improve credit performance, and build stronger portfolios. By turning complex data into actionable intelligence, we empower lenders to operate with clarity, make confident decisions, and deliver results—efficiently and at scale.

Our single-platform solution is built on explainable AI, enabling banks to move beyond legacy systems and into a new era of speed, transparency, and innovation.

From origination to monitoring, RDC.AI brings consistency, intelligence, and trust to every part of the lending lifecycle.

Because when you know more, you can do more—and grow more.

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