ETL Data - Senior Engineer
Iris Software · Noida · onsite · Posted 41d ago
Undisclosed5–10 yrsfulltimeSourced from
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About this role
ETL Data - Senior Engineer
Location: Noida
Company: Iris Software
Job Description Mandatory Skills - Databricks Workflows
- Delta Lake on Databricks
- Snowflake
- PySpark
- Amazon Kinesis
- 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.
- Lead implementation of high-performance batch and streaming data pipelines.
- Design and optimize event-driven data architectures using Apache Kafka or Amazon Kinesis.
- Define data streaming standards, integration frameworks, and scalable processing patterns.
- Architect workflow orchestration solutions using Apache Airflow or Databricks Workflows.
- 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.
- Drive development of business-focused data products by improving data quality, discoverability, usability, documentation, and trusted data consumption across analytical platforms.
- 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.
- Mentor team members on PySpark, Snowflake, Delta Lake, Kafka, Kinesis, Airflow, and data engineering best practices.
- Collaborate with various teams and stakeholders to support end-to-end data platform delivery.
- 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.
- Big Data - Big Data - Pyspark
- Data AI - Data Engineering - Data Quality Validation
- Data Science and Machine Learning - Data Science and Machine Learning - Python
- Database - Database Programming - SQL
- Data Science and Machine Learning - Data Science and Machine Learning - Apache Spark
- Cloud - AWS - AWS SNS, AWS SQS, AWS Kinesis
- Data Science and Machine Learning - Data Science and Machine Learning - Databricks
- Data AI - ETL OTHERS - Snowflake
- Data AI - BI Reporting and Data Visualization - ETL Concepts
- Data AI - ETL OTHERS - AWS Glue
- Cloud - AWS - Dynamo DB, Amazon Aurora, Amazon RDS
- Data AI - Database - MS SQL
- Database - Database Programming - PL/SQL
- Beh - Communication and collaboration
Sourced from naukri
Skills
Aws GlueAutomationApache SparkData ingestionApache KafkaAuroraaws kinesisApacheamazon web servicesAmazon RDSapache airflowAnalyticsbusiness automationBI ReportingData EngineeringcloudArchitectureBig DataArtificial Intelligence
About Iris Software
Iris Software
IT Services & Consulting
4.3k+
employees
1991
35 years old
Edison, New Jersey
United States (USA)
About
IT company for best tech-solutions to enhance business operations
Industries
IT Services & Consulting
Employee ratings
1,895 reviews
3.8/ 5
Culture
3.7
Career growth
3.3
Work-life
3.8
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