We are seeking a Data Engineer with hands-on experience in AWS and Snowflake to build, maintain, and optimize scalable data pipelines and cloud data warehouse solutions. The candidate will work with analysts, application teams, and business stakeholders to deliver reliable, secure, and analytics-ready data.
Design, develop, and maintain ETL/ELT pipelines to ingest data from databases, APIs, files, and enterprise applications.
Build and support AWS data solutions using Amazon S3, AWS Glue, AWS Lambda, Amazon CloudWatch, and IAM.
Develop Snowflake tables, views, data models, and transformation workflows for reporting and analytics.
Implement and manage data loading into Snowflake using stages, file formats, COPY INTO, and Snowpipe.
Develop incremental data processing workflows using Snowflake Streams and Tasks.
Write and optimize complex SQL queries and Python scripts for data processing and automation.
Implement data quality checks, reconciliation, monitoring, error handling, and recovery mechanisms.
Optimize Snowflake query performance, warehouse sizing, and compute costs.
Apply security controls, including role-based access, encryption, and appropriate handling of sensitive data.
Collaborate with business and analytics teams to translate requirements into technical solutions.
Maintain documentation for data pipelines, mappings, models, and operational procedures.
Participate in code reviews, testing, CI/CD, and production support.
Required Skills and Qualifications
Strong hands-on experience in data engineering with AWS and Snowflake.
Strong SQL and Python programming skills.
Experience with Amazon S3, AWS Glue, AWS Lambda, IAM, and CloudWatch.
Practical knowledge of Snowflake architecture, virtual warehouses, stages, Snowpipe, Streams, and Tasks.
Experience developing ETL/ELT pipelines, incremental loads, and change data capture (CDC).
Understanding of data warehousing concepts, dimensional modeling, and star and snowflake schemas.
Experience processing structured and semi-structured data, including JSON, CSV, and Parquet.
Familiarity with workflow orchestration tools such as Apache Airflow, Amazon MWAA, or AWS Step Functions.
Experience with Git, automated testing, and CI/CD practices.
Strong troubleshooting, communication, and collaboration skills.
Preferred Qualifications
Experience with dbt for data transformation and testing.
Experience with PySpark, Apache Spark, or Amazon EMR.
Exposure to streaming technologies such as Amazon Kinesis or Apache Kafka.
Experience with infrastructure as code using Terraform or AWS CloudFormation.
Familiarity with data governance, metadata management, and data lineage.
Relevant AWS or Snowflake certifications.
