Senior Data Engineer

Senior Data EngineerLocation: Noida, UP, India Job Description Mandatory Skills: Databricks Workflows,PySpark,Delta Lake on Databricks,Apache Kafka Additional Skills: NA Key Responsibilities: Design scalable data engineering solutions using PySpark and modern distributed data processing frameworks. Define data ingestion, transformation, and processing architectures aligned with business and analytical objectives. Design and optimize Snowflake or Delta Lake on Databricks solutions to support enterprise-scale data platforms. Define data streaming standards, integration frameworks, and scalable processing patterns. Establish monitoring, scheduling, and operational controls for reliable pipeline execution. Drive data quality, validation, reconciliation, and governance practices across data engineering solutions. Design data engineering solutions following modern Lakehouse architecture principles, data observability practices, and platform engineering standards to improve scalability, reliability, and operational visibility. Promote responsible use of AI-assisted engineering capabilities to improve development productivity, testing, documentation, and engineering quality. Review data pipeline designs and implementations to ensure adherence to engineering, scalability, and performance standards. Troubleshoot complex data processing, workflow, and streaming platform issues through detailed root cause analysis. Collaborate with various teams and stakeholders to support end-to-end data platform delivery. Behavioral Competencies: Demonstrates strong ownership while driving data engineering excellence. Collaborate effectively with various teams and business stakeholders to ensure smooth delivery. Promotes quality-focused engineering through proactive validation, optimization, and continuous improvement. Apply strong analytical thinking to evaluate complex data engineering and platform challenges. Demonstrate adaptability while managing evolving technologies, data ecosystems, and business requirements. Communicates effectively regarding delivery status, risks, dependencies, and improvement opportunities. Maintains high attention to detail across data architecture, pipeline design, testing, and implementation activities. Encourages continuous improvement in data engineering practices and platform operations. Supports knowledge sharing and mentoring to strengthen team capabilities. Balances scalability, performance, reliability, and business priorities while driving delivery excellence. Promotes innovation by adopting modern data engineering practices, platform engineering principles, and AI-assisted development approaches to improve engineering productivity and solution quality. Mandatory Competencies DevOps/Configuration Mgmt - DevOps/Configuration Mgmt - GitLab,Github, Bitbucket Data Science and Machine Learning - Data Science and Machine Learning - Python Big Data - Big Data - Pyspark Database - Database Programming - SQL Data Science and Machine Learning - Data Science and Machine Learning - Apache Spark Data AI - Data Engineering - Data Quality Validation Data AI - Data Engineering - Apache Kafka Data Science and Machine Learning - Data Science and Machine Learning - Databricks Beh - Communication and collaboration