Principal Consulting AI / Data Engineer at DyFlex Solutions designing and building scalable data and AI solutions. Leading engagements with executive stakeholders in a hybrid role based in Sydney, Australia.
Responsibilities
Design, build, and maintain scalable data and AI solutions using Databricks, cloud platforms, and modern frameworks
Lead solution architecture discussions with clients, ensuring alignment of technical delivery with business strategy
Present to and influence executive-level stakeholders, including boards, C-suite, and senior directors
Translate highly technical solutions into clear business value propositions for non-technical audiences
Mentor and guide teams of engineers and consultants to deliver high-quality solutions
Champion best practices across data engineering, MLOps, and cloud delivery
Build DyFlex’s reputation as a trusted partner in Data & AI through thought leadership and client advocacy
Requirements
Proven expertise in delivering enterprise-grade data engineering and AI solutions in production environments
Strong proficiency in Python and SQL, plus experience with Spark, Airflow, dbt, Kafka, or Flink
Experience with cloud platforms (AWS, Azure, or GCP) and Databricks
Ability to confidently communicate and present at C-suite level, simplifying technical concepts into business impact
Track record of engaging senior executives and influencing strategic decisions
Strong consulting and stakeholder management skills with client-facing experience
Background in MLOps, ML pipelines, or AI solution delivery highly regarded
Degree in Computer Science, Engineering, Data Science, Mathematics, or a related field
Benefits
Work with SAP’s latest technologies on cloud as S/4HANA, BTP and Joule, plus Databricks, ML/AI tools and cloud platforms
A flexible and supportive work environment including work from home
Competitive remuneration and benefits including novated lease, birthday leave, salary packaging, wellbeing programme, additional purchased leave, and company-provided laptop
Comprehensive training budget and paid certifications (Databricks, SAP, cloud platforms)
Structured career advancement pathways with opportunities to lead large-scale client programs
Exposure to diverse industries and client environments, including executive-level engagement
Join a renowned organisation delivering projects to some of Australia’s leading enterprises
Data Engineer managing payment processing and data accuracy while collaborating with financial teams. Building and optimizing data pipelines for transactional data in a hybrid work environment.
Data Engineer building analytical tools for Dry Bulk market data operations at Kpler. Join a team of over 700 experts transforming data into actionable strategies.
Data Engineer developing tools for maintaining data integrity in cargo tracking at Kpler. Collaborating with analysts and engineers to enhance data quality management.
Lead Azure Data Engineer designing and optimizing data ecosystems on Microsoft Cloud. Responsible for building scalable data platforms and pipelines for analytics and reporting.
Data Engineer providing support for IBM DataStage ETL jobs at Callibrity. Collaborating with stakeholders and working to modernize technology solutions in a hybrid work environment.
Cloud Data Engineer implementing tailored solutions for Volkswagen Group data processing. Building ETL/ELT pipelines while collaborating with technical experts.
Data Engineer responsible for building scalable data infrastructure that supports data - driven decisions. Collaborating with team to maintain systems and unlock data value for organizations.
Data Engineer designing and optimizing data pipelines using Databricks and Google Cloud Platform. Collaborating with analysts and scientists to deliver high - quality data products.
Associate Data Engineer supporting privacy engineering controls and executing privacy impact assessments in a financial services company. Collaborating across business units to ensure alignment with privacy regulations.
Data Engineer at CVS Health optimizing data pipelines and analytical models. Driving data - driven decisions with healthcare data for improved business outcomes.