Senior Data EngineerLocation: Noida, UP, IndiaJob 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 robust 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

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