Azure Data Factory Skills Needed

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 Azure Data Factory-Senior Application Developer


Role- Application Developer

Job Description – Azure Data Engineer (ADF | ADLS | Databricks | Synapse)

Job Title:  Azure Data Engineer – Azure Data Factory, Azure Data Lake, Azure Databricks

Experience: 4+ years (flexible based on skill fit)

 

Role Summary:

  • You will design and build Azure data pipelines and data platforms.
  • You will ingest data from multiple sources, transform it, and deliver curated datasets.
  • You will support analytics and AI/ML use cases using Azure services.
  • You will ensure performance, security, and reliability of data workloads.

 

Key Responsibilities:

  1. Data Ingestion & Pipeline Development (ADF)
  • Design and develop pipelines in Azure Data Factory (ADF).
  • Build batch and incremental loads from sources like APIs, databases, files, and cloud storage.
  • Use Linked Services, Datasets, Triggers, Integration Runtime properly.
  • Implement pipeline monitoring, alerts, and failure handling.
  • Follow best practices for parameterization and reusable templates.

 

  1. Data Lake & Storage Management (ADLS / Azure SQL)
  • Design data lake structure in Azure Data Lake Storage (ADLS Gen2).
  • Manage zones like raw, curated, and consumption layers.
  • Ensure proper partitioning, file formats, and naming standards.
  • Work with Azure SQL Database for serving and operational data needs.
  • Implement data retention and lifecycle policies where needed.

 

  1. Data Transformation & Big Data Processing (Databricks / Spark)
  • Develop PySpark / Spark SQL notebooks in Azure Databricks.
  • Build scalable transformations and data quality checks.
  • Optimize cluster usage and job performance to control cost.
  • Implement delta lake patterns (if used): Delta tables, MERGE, upserts.
  • Support orchestration of Databricks jobs via ADF or workflow tools.

 

  1. Data Warehousing & Analytics (Synapse)
  • Build and manage analytics solutions using Azure Synapse Analytics.
  • Design warehouse objects and implement data loading patterns.
  • Support performance tuning for queries and workloads.
  • Provide curated datasets for reporting and downstream applications.

 

  1. Data Modeling & ETL Design
  • Create strong data models (star schema / dimensional model) based on reporting needs.
  • Define data mappings, transformations, and dependencies.
  • Ensure data consistency across lake, warehouse, and BI layers.
  • Maintain documentation like source-to-target mapping and pipeline runbook.

 

  1. AI/ML Enablement (Azure Machine Learning)
  • Support ML use cases with data preparation and feature pipelines.
  • Work with Azure Machine Learning for training and deployment support.
  • Build Python scripts for model development when needed.
  • Use libraries like Scikit-learn (must) and TensorFlow / PyTorch (good to have).
  • Work closely with Data Scientists to productionize pipelines.

 

  1. SQL, Python & Engineering Practices
  • Write optimized SQL for data validation, reconciliation, and transformations.
  • Write clean Python code for automation and data processing tasks.
  • Use Git for version control and follow branching/review process.
  • Support CI/CD pipelines for data deployments where available.

 

  1. Security, Governance & Compliance
  • Implement security best practices for data access and storage.
  • Work with RBAC, Managed Identity, Key Vault (as per project).
  • Ensure compliance with client data handling policies.
  • Support audit requirements and access reviews.

 

  1. Delivery & Support
  • Work in agile teams and deliver tasks as per sprint plan.
  • Provide estimates and support release planning.
  • Handle production issues, RCA, and preventive actions.
  • Coordinate with platform, network, and security teams when needed.

 

Must-Have Skills (Primary):

  • Azure Data Factory (ADF) – strong hands-on pipeline development
  • Azure Data Lake Storage (ADLS Gen2) – storage design and management
  • Azure Databricks – PySpark/Spark SQL development and tuning
  • Azure Synapse Analytics – warehouse/analytics experience
  • SQL – strong in complex queries and troubleshooting
  • Python – scripting + data processing (ML exposure is a plus)
  • Data Modeling & ETL – warehouse concepts and end-to-end understanding
  • Experience integrating multiple Azure services for end-to-end data flow

 

Good-to-Have Skills (Secondary):

  • Power BI – datasets, models, dashboards, performance basics
  • Azure Functions / Logic Apps – basic automation exposure
  • Azure Cognitive Services – basic awareness (optional)
  • Big data background: Hadoop basics, strong Spark understanding
  • Agile/Scrum – sprint execution and collaboration
  • Data security basics: encryption, access control, compliance awareness
  • CI/CD exposure for data (Azure DevOps pipelines)

 

Tools & Technologies:

  • Azure: ADF, ADLS Gen2, Databricks, Synapse, Azure SQL, Azure ML
  • Languages: Python, SQL, PySpark
  • Dev Tools: Git, Azure DevOps/Jira (as applicable)
  • Monitoring: ADF monitor, Databricks job runs, Log Analytics (if enabled)

 

Qualification

  • BE/BTech/MCA or equivalent experience

 

Soft Skills

  • Clear communication with business and technical teams.
  • Ownership mindset and strong troubleshooting.
  • Good documentation and disciplined delivery.
  • Works well in multi-team and multi-region setup.

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