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Tools in this posting

Python
SQL
Azure
Databricks
dbt
Delta
Hive
Spark
pandas
C#

Source

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)

Job description

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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)