Software Engineer
Accountable for delivering tested and reviewed software changes to data products and AI services, and for diagnosing their faults.
3tasks in this job
10skills it uses
10tools named
The skills this job leans on most
- 1Writing maintainable code for analytical and data workNeeded in 3 of 3 tasks
- 2Reviewing human- and AI-generated code changes for correctness and riskNeeded in 3 of 3 tasks
- 3Testing changes to data services and business systems for business meaning and downstream impactNeeded in 3 of 3 tasks
ToolsDatadogAmazon Web Services (console, Cost Explorer, CloudWatch)JiraGitHub ActionsGit / GitHubVisual Studio CodePythonPostgreSQLSQL (query language)GitHub Copilot
All 3 tasks, where people get stuck and all 10 skills
Tasks
- Investigate application or API faults
- Prepare and verify a data-service release or migration for change approval
- Deliver a tested software or API change to a data or AI service
Where people get stuck
- Understanding and interrogating data and application APIs
- Debugging data and application services from evidence
All skills
- Testing changes to data services and business systems for business meaning and downstream impact
- Writing maintainable code for analytical and data work
- Escalating unresolved risks with evidence and clear ownership
- Identifying keys and testing referential integrity
- Interpreting policy scope and delegated authority limits
- Preparing change records with impact assessment and rollback
- Recognising statutory incident reporting duties and deadlines
- Repairing data defects reversibly under owner authority
- Tracing data lineage upstream and downstream
- Writing verifiable requirements and acceptance criteria
Paths to start
Coming soonEngineering Foundations for Data and AI WorkAnalytics Engineering: Tested, Version-controlled Transformation
3tasks in this job
33skills it uses
11tools named
The skills this job leans on most
- 1Understanding and interrogating data and application APIsNeeded in 3 of 3 tasks
- 2Writing maintainable code for analytical and data workNeeded in 3 of 3 tasks
- 3Debugging data and application services from evidenceNeeded in 3 of 3 tasks
- 4Reviewing human- and AI-generated code changes for correctness and riskNeeded in 3 of 3 tasks
- 5Testing changes to data services and business systems for business meaning and downstream impactNeeded in 3 of 3 tasks
ToolsDatadogElasticsearch / Elastic Stack (Kibana)SQL (query language)Apache KafkaPythonGitHub Actions+ 5 more
All 3 tasks, where people get stuck and all 33 skills
Tasks
- Investigate application or API faults
- Prepare and verify a data-service release or migration for change approval
- Deliver a tested software or API change to a data or AI service
Where people get stuck
All skills
- Debugging data and application services from evidence
- Reviewing human- and AI-generated code changes for correctness and risk
- Testing changes to data services and business systems for business meaning and downstream impact
- Understanding and interrogating data and application APIs
- Writing maintainable code for analytical and data work
- Assessing change risk, collisions and go or no-go
- Assessing data-service continuity and recoverability
- Building bounded, secure data-service APIs
- Building data protection by design into data products and pipelines
- Coordinating accountable handoffs between teams
- Designing relational schemas: keys, constraints and normalisation
- Diagnosing and remedying query and database performance
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Identifying keys and testing referential integrity
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Mapping proposed process changes and control impacts
- Monitoring data quality and freshness in production
- Planning and validating controlled data migrations
- Preparing change records with impact assessment and rollback
- Rating risks against appetite and choosing proportionate treatment
- Reading and tracing unfamiliar code
- Recognising and containing suspected personal data breaches
- Recognising statutory incident reporting duties and deadlines
- Repairing data defects reversibly under owner authority
- Running incident command: severity, timeline and status updates
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Writing verifiable requirements and acceptance criteria
All tools
- Datadog
- Elasticsearch / Elastic Stack (Kibana)
- SQL (query language)
- Apache Kafka
- Python
- GitHub Actions
- Azure DevOps
- PostgreSQL
- Git / GitHub
- Grafana
- GitHub Copilot
Paths to start
Coming soonEngineering Foundations for Data and AI WorkAnalytics Engineering: Tested, Version-controlled TransformationData Product Reliability: Release Gates, Service Levels and Incidents
4tasks in this job
42skills it uses
10tools named
The skills this job leans on most
- 1Understanding and interrogating data and application APIsNeeded in 4 of 4 tasks
- 2Writing maintainable code for analytical and data workNeeded in 4 of 4 tasks
- 3Debugging data and application services from evidenceNeeded in 4 of 4 tasks
- 4Reviewing human- and AI-generated code changes for correctness and riskNeeded in 4 of 4 tasks
- 5Testing changes to data services and business systems for business meaning and downstream impactNeeded in 4 of 4 tasks
ToolsDatadogKubernetesApache KafkaSQL (query language)GitHub ActionsJira+ 4 more
All 4 tasks, where people get stuck and all 42 skills
Tasks
- Investigate application or API faults
- Prepare and verify a data-service release or migration for change approval
- Deliver a tested software or API change to a data or AI service
- Set engineering standards for testing, change, data handling and AI-assisted code
Where people get stuck
All skills
- Debugging data and application services from evidence
- Reviewing human- and AI-generated code changes for correctness and risk
- Testing changes to data services and business systems for business meaning and downstream impact
- Understanding and interrogating data and application APIs
- Writing maintainable code for analytical and data work
- Applying data classification and handling rules
- Assessing change risk, collisions and go or no-go
- Assessing data-service continuity and recoverability
- Building bounded, secure data-service APIs
- Building data protection by design into data products and pipelines
- Coordinating accountable handoffs between teams
- Designing relational schemas: keys, constraints and normalisation
- Diagnosing and remedying query and database performance
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Mapping proposed process changes and control impacts
- Monitoring data quality and freshness in production
- Planning and validating controlled data migrations
- Preparing change records with impact assessment and rollback
- Rating risks against appetite and choosing proportionate treatment
- Reading and tracing unfamiliar code
- Recognising and containing suspected personal data breaches
- Recognising statutory incident reporting duties and deadlines
- Repairing data defects reversibly under owner authority
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing verifiable requirements and acceptance criteria
All tools
- Datadog
- Kubernetes
- Apache Kafka
- SQL (query language)
- GitHub Actions
- Jira
- Git / GitHub
- PostgreSQL
- GitHub Copilot
- Python
Paths to start
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonEngineering Foundations for Data and AI WorkAnalytics Engineering: Tested, Version-controlled Transformation
About the role
Accountable for delivering tested and reviewed software changes to data products and AI services, and for diagnosing their faults. Decides how a change is built, tested and rolled back within the service's design. Data and AI judgement sits in protecting data integrity at interfaces and APIs and in checking AI-assisted code before it ships.
Also called Software Developer, Full Stack Developer, Python Developer, .NET Developer