Sr. Data Engineer
New
Based in India, flexible working hours with dedicated collaboration time aligned with U.S.-based teamsFull-TimeSenior
Salary4,200,000 INR per year
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Job Details
- Experience
- 6–8+ years
- Required Skills
- AWSPythonSQLKafkaSnowflakeData modelingDatabricksPySpark
Requirements
- 6–8+ years of experience in data engineering within production data environments.
- Expert-level proficiency in Python and PySpark.
- Strong SQL skills.
- Hands-on experience building data solutions using Snowflake or Databricks within an AWS ecosystem.
- Proven experience developing streaming applications using AWS Kinesis, Kafka, or similar.
- Deep knowledge of data modeling, data lake architectures, and ETL/ELT processes.
- Experience implementing automated data validation frameworks and profiling solutions.
- Familiarity with ML data workflows, feature stores, and AI/ML platform integrations.
- Strong documentation skills for technical specifications and operational guidelines.
- Excellent communication skills for technical and non-technical stakeholders.
- Relevant certifications (e.g., SnowPro Core, Databricks Certified Data Engineer, AWS Certified Data Engineer) are a plus.
Responsibilities
- Build, optimize, and maintain complex data pipelines using AWS Glue, Step Functions, and Databricks Workflows.
- Design scalable data architectures using Medallion Architecture and Dimensional Modeling.
- Manage cloud-based storage using AWS S3 and optimize formats like Delta, Iceberg, and Parquet.
- Develop real-time ingestion and streaming solutions using AWS Kinesis and Kafka.
- Implement Change Data Capture (CDC) solutions using Debezium or Fivetran.
- Own data quality and validation processes, including automated testing and monitoring.
- Establish data contracts, schema evolution practices, and proactive alerting mechanisms.
- Build ML-ready datasets and support feature engineering for machine learning workflows.
- Collaborate with Product, Engineering, Analytics, and ML teams.
- Mentor junior engineers and promote best practices in SQL optimization and documentation.
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