Data Engineer Advisor
At a glance
Mid-level Data Engineering role at NTT DATA. Hyderabad.
Pay not stated Data Engineering salary
dataroles summary, based on the employer's posting.
What you'll do
- Move SSIS workloads into Azure Databricks and PySpark Lakehouse environments while keeping existing SQL Server systems running
- Design warehouse and Bronze, Silver, and Gold data layers, then build batch and real-time pipelines
- Guide data engineers through code reviews and development standards while continuing to write and optimize code
- Connect Azure Data Factory with databases, cloud storage, and Databricks, and improve Spark job performance
- Set up data checks, audit logging, and automated code deployment for data environments
What you bring
- Eight to ten years of hands-on SSIS experience, with strong SQL Server and T-SQL skills
- Production-level Azure Databricks, Delta Lake, and ADLS Gen2 experience, plus advanced Python and PySpark ability
- Practical Azure Data Factory orchestration experience and knowledge of relational and dimensional data models
- Experience leading through code while remaining hands-on, with Git-based CI/CD and secure data handling skills
Who this fits
This mid-level role suits a hands-on data technical lead who can mentor engineers while still designing and coding pipelines. The work is based in Hyderabad, India.
From the employer
Job Summary
A highly skilled 8 to 10 years of experience on SSIS. SQL Server, Azure Databricks, Pyspark, Hands-On Data Tech Lead to drive the modernization and scaling of our enterprise data platform to provide technical leadership while remaining deeply technical and actively writing code. Also lead the migration, integration, and development of data pipelines across our legacy SQLServer/SSIS environments and our modern cloud infrastructure built on Azure Databricks and PySpark and act as the principal architect and developer for end-to-end ELT/ETL pipelines, ensuring optimal performance, data governance, and scalability.
- Lead the migration of legacy SQL Server Integration Services (SSIS) packages to modern, cloud-native Azure Databricks / PySpark Lakehouse environments.
- Architect end-to-end Modern Data Warehouse (MDW) and Medallion architecture systems (Bronze, Silver, Gold layers) within Azure.
- Mentor and guide a team of data engineers, conducting stringent code reviews, enforcing design patterns, and establishing development standards.
- Collaborate with stakeholders including Data Architects, Business Analysts, and DevOps teams to translate complex business requirements into technical blueprints.
- Develop and optimize scalable, modular, high-performance batch and real-time ETL/ELT pipelines using PySpark on Azure Databricks cluster environments.
- Maintain and refactor existing on-premise SSIS packages and complex T-SQL code (stored procedures, views, performance tuning) to ensure business continuity during transition phases.
- Implement Delta Lake features, managing transactions, schema evolution, and time-travel querying functionalities.
- Orchestrate workflows seamlessly by integrating Azure Data Factory (ADF) with on-premise databases, cloud storage (ADLS Gen2), and Databricks activities.
- Optimize Spark configurations and cluster utilization to drastically reduce cloud compute overhead and job execution windows.
- Enforce data quality frameworks encompassing automated data validation, error reconciliation, and operational audit logging.
- Implement CI/CD automations via Azure DevOps or GitHub Actions to manage environments, code repositories (Git), and deployment pipelines.
Skill Requirements
Technical Skills
- Azure Big Data Stack: Deep, production-level knowledge of Azure Databricks, Delta Lake, Unity Catalog, and Azure Data Lake Storage (ADLS Gen2).
- Programming Languages: Advanced proficiency in Python and PySpark core APIs (Spark SQL and DataFrames).
- Legacy ETL & Databases: Mastery of SQL Server, Advanced T-SQL writing, and extensive experience configuring, deploying, and troubleshooting SSIS packages.
- Orchestration: Hands-on experience mapping dependencies and scheduling triggers using Azure Data Factory (ADF).
- Data Modeling: Expert understanding of relational (OLTP) and dimensional (OLAP) schemas, explicitly Star and Snowflake architectures.
- DevOps & Security: Competency with Git branching, CI/CD automated release deployments, and secure data handling mechanisms (Azure Key Vault, network isolation).
Soft Skills & Leadership
- Proven track record as a coding lead who splits time between management/design and hands-on execution.
- Excellent cross-functional communication skills, comfortably discussing data strategies with both executive leaders and junior technical staff.
- Strong debugging and root-cause analysis (RCA) skills for multi-tier platform failures.
- Preferred Certifications: Microsoft Certified: Azure Data or Databricks
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