Data Engineer
At a glance
Mid-level Data Engineering role at Fa Ewjt Saasfaprod1. Gurugram · full-time.
Pay not stated Data Engineer salary
dataroles summary, based on the employer's posting.
What you'll do
- Build data pipelines and processing workflows, then improve how data is processed and stored
- Check data solutions against performance standards and resolve data issues with scientists and analysts
- Provide technical support and work with stakeholders to meet data requirements
What you bring
- At least eight years in data engineering or ETL, including four or more years leading Azure cloud ETL work
- Azure certifications are required, with Azure Analytics Specialty or Solutions named as examples
- Hands-on knowledge of Azure data sources, ADF, Databricks connectors, Spark, Delta Lake and Azure monitoring tools
Who this fits
This mid-level, full-time role is based in Gurugram, India, with a hybrid work arrangement. It suits someone with substantial data engineering experience and a background leading Azure cloud ETL work.
From the employer
Data Engineers design and build data systems and pipelines. Responsibilities include developing data processing workflows, optimizing data storage, and ensuring data accuracy. You will collaborate with data scientists and analysts to meet data requirements and resolve data issues. Strong experience in data engineering and problem-solving skills are required.
Design and implement data pipelines, Optimize data processing and storage, Ensure data solutions meet performance standards, Provide technical support, Collaborate with stakeholders.
8+ years in data engineering/ETL roles, with at least 4+ years in Azure cloud ETL leadership. Azure certifications (e.g., Azure Analytics Specialty, Solutions
Required Qualifications:
- Azure Data Sources: Azure Data Lake Storage (ADLS), Blob Storage, Azure SQL Database, Synapse Analytics. External Sources: APIs, on-prem databases, flat files (CSV, Parquet, JSON).
- Tools: Azure Data Factory (ADF) for orchestration, Databricks connectors.
- Apache Spark: Strong knowledge of Spark (PySpark, Spark SQL) for distributed processing.
- Data Cleaning & Normalization: Handling nulls, duplicates, schema evolution.
- Performance Optimization: Partitioning, caching, broadcast joins.
- Delta Lake: Implementing ACID transactions, time travel, and schema enforcement.
- Azure Data Factory (ADF): Building pipelines to orchestrate Databricks notebooks.
- Azure Key Vault: Secure credential management.
- Azure Monitor & Logging: For ETL job monitoring and alerting.
- Networking & Security: VNET integration, private endpoints.
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Data Engineering
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