Data Engineer
Data Engineer/Developer
Role Summary
Design, build, and optimize Azure data pipelines and lakehouse solutions.
Deliver secure, reliable datasets with strong governance, automation, and
documentation. Collaborate across teams and contribute to standards in an Agile
setting.
Must-Have (Day 1)
- Experience:
4–6 years in data engineering
- Core
Platform: Databricks with Python, Spark, Pandas (notebooks and modular
code)
- Orchestration:
Azure Data Factory (pipelines, integration runtimes); ingest from diverse
sources
- Lakehouse:
Delta Lake fundamentals; Medallion architecture (bronze/silver/gold) in
production
- Storage/SQL/Performance:
Azure Data Lake Storage (ADLS); strong SQL; performance-aware design
- Data
Patterns: ETL/ELT; data modeling (e.g., dimensional/star schema)
- DevOps
& Security: CI/CD for data projects (Azure DevOps or GitHub
Enterprise); familiarity with Azure Entra ID for SSO/RBAC; secure
workspace/data access
- Quality
& Observability: Data validation/testing, code reviews, and basic
monitoring/alerting for jobs/pipelines
- Ways of
Working: Agile/Scrum (Jira/Confluence); clear pipeline and data contract
documentation
- Collaboration:
Effective stakeholder engagement; support/mentor junior team members;
clear communication
- Generative
AI (Day 1):
- Prompt
design for data tasks (ingestion, transformations, documentation) with
clear objectives and constraints
- Use of
Copilot/ChatGPT to scaffold notebooks/jobs, generate tests, and optimize
SQL/Spark—validates outputs before merging
Nice-to-Have (Train within 60–90 days)
- Unity
Catalog migration (Hive to Unity) and permissions/governance
- Databricks
DevOps (cluster configuration, secret management, workspace automation)
- Azure
Functions (C# or Python) for orchestration/integration
- Synapse
dedicated SQL pools or dbt; Delta Live Tables
- Financial
services domain exposure
Shared Expectations
- Work
independently with minimal supervision while contributing to team outcomes
- Commitment
to secure practices and production-grade reliability
- Continuous
improvement mindset and willingness to learn new tools/technologies
- Willingness
to work within regulated environment controls and policies
- Use
Generative AI responsibly to improve velocity and quality (simple,
structured prompts; guardrails; validate AI-assisted outputs before
adoption)