NTT DATA is seeking a skilled Technology Consultant specializing in Data Engineering. This role is vital for designing, developing, and maintaining robust enterprise data pipelines and platforms. The ideal candidate will possess strong practical skills in Python, SQL, ETL/ELT processes, cloud data technologies, and modern data engineering tools. Collaboration with cross-functional teams, including Data Architects, Data Scientists, and Business Analysts, will be key to building reliable data solutions that prioritize data quality, performance, security, and scalability.

Key responsibilities include designing and implementing scalable ETL/ELT pipelines for diverse data types, building robust data ingestion and transformation solutions from multiple sources, and developing optimized SQL queries and Python scripts. You will also be involved in constructing and managing data warehouses, data lakes, and Lakehouse architectures, developing both batch and real-time data streams, and implementing complex data mapping and transformation logic. Data quality assurance through profiling, cleansing, validation, and reconciliation is essential. Performance, scalability, reliability, and cost efficiency of data pipelines will require continuous optimization. Troubleshooting pipeline failures and data quality issues, translating business requirements into technical solutions, and implementing data orchestration, monitoring, and alerting systems are also integral. Supporting production pipelines, conducting root cause analysis, and participating in code reviews, testing, CI/CD deployments, and production releases are expected. Maintaining comprehensive documentation for data mappings, technical designs, and operational procedures is crucial.

A minimum of 5 years of experience in Data Engineering, ETL/ELT development, and enterprise data integration is required. Candidates must have at least 4 years of experience working with Python, SQL, data transformation, and large-scale data processing. Additionally, 3+ years of experience with cloud data services on AWS, Azure, or Google Cloud is necessary, along with 3+ years of experience using modern data platforms such as Spark/PySpark, Databricks, or Snowflake. Core technical competencies include programming languages like Python and SQL, data processing with Spark and PySpark, data platforms like Databricks and Snowflake, ETL/ELT methodologies, cloud platforms (AWS, Azure, GCP), data architecture principles (Data Warehouse, Data Lake, Lakehouse), orchestration tools (Airflow, Databricks Workflows), streaming technologies (Kafka, Kinesis, Event Hubs, Pub/Sub), development practices (Git, CI/CD, Automated Testing), and data quality management (Validation, Reconciliation, Profiling, Monitoring).