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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and maintaining data pipelines using Python, PySpark, and SQL, while effectively managing end-to-end data solutions and stakeholder communications. Proficient in data modeling practices and deploying machine learning models in production environments, ensuring data quality and governance.
Highest-signal resume keywords
Data Pipeline DevelopmentMachine Learning Model DeploymentData Modeling and Data MartsStakeholder ManagementExperience with Databricks
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonSQLPySparkData ModelingMachine Learning EngineeringData Pipeline OptimizationData Quality ChecksOrchestration and MonitoringInfrastructure as CodeCI/CD for Data Engineering
Soft Skills
Clear CommunicationStakeholder EngagementAccountabilityMentoring
Tools & Technologies
DatabricksDelta LakeUnity CatalogMLflowPresetTableauMetabasePower BI
Industry Keywords
FintechFinancial ServicesPaymentsGDPRData PrivacyData Governance
Tech Stack
Tools & technologiesCloudPySparkPythonSparkSQLTableauUnity
About the role
Key responsibilities & impact- Design, build, and maintain data pipelines and transformations on Databricks using Python, PySpark, and SQL
- Deploy, operationalize, and monitor machine learning models in production environments
- Build and maintain data marts as the primary source of truth for key business domains
- Apply data modelling practices such as dimensional modelling and medallion architecture
- Implement data quality checks, monitoring, and alerting
- Optimize pipeline performance and cost-efficiency
- Lead and contribute to engineering standards and process improvements across the data team
- Act as the primary data partner for assigned business areas
- Manage end-to-end solutions from scoping and discovery through design, delivery, user adoption, and iteration
- Gather and translate business requirements into technical solutions
- Communicate with engineering peers and commercial stakeholders to define priorities and manage trade-offs
- Identify opportunities where data or machine learning can improve decisions, efficiency, or customer outcomes
- Build and maintain datasets powering reporting and dashboards in Preset
- Onboard and support users on Preset
- Promote data definitions, documentation, governance, and self-service data practices
Requirements
What you’ll need- 5+ years of experience as a Data/ML Engineer
- Track record of independently owning data/ML solutions from scoping to adoption
- Proficiency in Python, SQL, and PySpark/Spark
- Experience with modern data platforms such as Databricks, including Delta Lake, Unity Catalog, MLflow, and workflows
- Stakeholder management skills across commercial and engineering discussions
- Data modelling skills and experience designing data marts for analytics and reporting
- Experience building production pipelines with orchestration, testing, monitoring, and version control
- Experience with BI tools such as Preset, Tableau, Metabase, or Power BI
- Demonstrated accountability for project outcomes and commercial impact
- Clear, structured communication in English
- Experience in ML Engineering, including deploying models to production, maintaining MLOps pipelines, and integrating models into data platforms
- Experience in fintech, financial services, or payments is nice to have
- Experience with sensitive or regulated data and data privacy and governance, such as GDPR, is nice to have
- Experience with infrastructure as code, cloud platforms, and data engineering CI/CD is nice to have
- Experience mentoring engineers or analysts is nice to have
- Interest in applying AI tools to improve data engineering productivity and quality is nice to have
Benefits
Comp & perks- Opportunities to learn, develop new skills, take on new challenges, and advance your career across functions and geographies
- Opportunity to shape solutions, challenge the status quo, and drive real impact
- Work alongside talented colleagues from diverse backgrounds and disciplines
- Culture encouraging experimentation, embracing change, and continuous improvement