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Employment: Unconfirmed

What you’ll work on

Develop modules/code for migration of existing Matillion jobs to AWS-native solutions.
Implement data quality, monitoring, alerting, security, governance, and compliance standards.
Design, develop, and promote scalable data pipelines using AWS Glue and Apache Airflow (and other basic AWS Services) - must have.

From the employer’s posting

Key Responsibilities Develop modules/code for migration of existing Matillion jobs to AWS-native solutions. Participate in Requirements Analysis of existing data processing jobs.

Data migration and modernization projects – good to have Implement data quality, monitoring, alerting, security, governance, and compliance standards. Collaborate with data architects and business teams to understand migration requirements and translate them into technical solutions.

Participate in Requirements Analysis of existing data processing jobs. Design, develop, and promote scalable data pipelines using AWS Glue and Apache Airflow (and other basic AWS Services) - must have. Build robust data ingestion, transformation, and orchestration frameworks on AWS.

Tools in this posting

AWS
Snowflake
Spark
Airflow
SQL
Python
PySpark

Source

We are seeking an experienced AWS Data Engineer to lead and execute a large-scale migration from Matillion ETL to a modern AWS-native data platform. The ideal candidate will possess deep expertise in Apache Airflow, AWS Glue, AWS Lambda and Snowflake, with a strong background in designing scalable data pipelines, cloud-based data integration, workflow orchestration, and data warehouse modernization. The candidate will work closely with business stakeholders, data architects, and engineering teams to migrate existing ETL workloads, optimize data processing frameworks, and establish best practices for cloud-native data engineering.

Key Responsibilities - Develop modules/code for migration of existing Matillion jobs to AWS-native solutions. - Participate in Requirements Analysis of existing data processing jobs.

- Participate in Requirements Analysis of existing data processing jobs. - Design, develop, and promote scalable data pipelines using AWS Glue and Apache Airflow (and other basic AWS Services) - must have. - Build robust data ingestion, transformation, and orchestration frameworks on AWS.

- Design, develop, and promote scalable data pipelines using AWS Glue and Apache Airflow (and other basic AWS Services) - must have. - Build robust data ingestion, transformation, and orchestration frameworks on AWS. - Create reusable and modular ETL/ELT components for enterprise data platforms.

- Collaborate with data architects and business teams to understand migration requirements and translate them into technical solutions. - Optimize data processing performance, cost, and scalability across AWS services. - Participate in CI/CD best practices and DevOps processes for data engineering workflows.

- Apache Airflow (DAG development, workflow orchestration, scheduling, monitoring) - AWS Glue (ETL jobs, Glue Studio, Crawlers, Data Catalog, Spark-based transformations) - Snowflake (Data Warehousing, SnowSQL, Snowpipe, Streams & Tasks, Performance Tuning)

- Snowflake (Data Warehousing, SnowSQL, Snowpipe, Streams & Tasks, Performance Tuning) - AWS Lambda and Step Functions (preferred) - Git experience (must-have)

Experience Requirements - 2+ years of intensive hands-on experience with AWS data services. - Proven experience migrating ETL platforms such as Matillion (or other ETL tools like DataStage) to AWS-native architectures (Good to have).

- 2+ years of intensive hands-on experience with AWS data services. - Proven experience migrating ETL platforms such as Matillion (or other ETL tools like DataStage) to AWS-native architectures (Good to have). - Strong experience developing workflows in Apache Airflow.

- Strong experience developing workflows in Apache Airflow. - Extensive experience building and optimizing AWS Glue jobs. - Hands-on experience with Snowflake stored procedures (must-have), performance tuning (good-to-have).

- AWS Glue (ETL jobs, Glue Studio, Crawlers, Data Catalog, Spark-based transformations) - Snowflake (Data Warehousing, SnowSQL, Snowpipe, Streams & Tasks, Performance Tuning) - AWS Lambda and Step Functions (preferred)

- Extensive experience building and optimizing AWS Glue jobs. - Hands-on experience with Snowflake stored procedures (must-have), performance tuning (good-to-have). - Strong understanding of cloud architecture, scalability, deployments.

- Git experience (must-have) Programming languages: Python, SQL, Spark (any 1 is must) PySpark, Matillion, Shell scripting (good to have)

Core Technologies - Apache Airflow (DAG development, workflow orchestration, scheduling, monitoring) - AWS Glue (ETL jobs, Glue Studio, Crawlers, Data Catalog, Spark-based transformations)

- Proven experience migrating ETL platforms such as Matillion (or other ETL tools like DataStage) to AWS-native architectures (Good to have). - Strong experience developing workflows in Apache Airflow. - Extensive experience building and optimizing AWS Glue jobs.

Programming languages: Python, SQL, Spark (any 1 is must) PySpark, Matillion, Shell scripting (good to have) Experience Requirements

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Status in our records: Live
First seen by us: Sep 5, 2026 · 42d ago
Recorded sightings: 9
Last seen by us: Oct 16, 2026

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Job description

View original posting ↗

We are seeking an experienced AWS Data Engineer to lead and execute a large-scale migration from Matillion ETL to a modern AWS-native data platform. The ideal candidate will possess deep expertise in Apache Airflow, AWS Glue, AWS Lambda and Snowflake, with a strong background in designing scalable data pipelines, cloud-based data integration, workflow orchestration, and data warehouse modernization.

The candidate will work closely with business stakeholders, data architects, and engineering teams to migrate existing ETL workloads, optimize data processing frameworks, and establish best practices for cloud-native data engineering.

Requirements

Key Responsibilities

Develop modules/code for migration of existing Matillion jobs to AWS-native solutions.
Participate in Requirements Analysis of existing data processing jobs.
Design, develop, and promote scalable data pipelines using AWS Glue and Apache Airflow (and other basic AWS Services) - must have.
Build robust data ingestion, transformation, and orchestration frameworks on AWS.
Create reusable and modular ETL/ELT components for enterprise data platforms.
Data modeling and warehousing concepts – must have
Data migration and modernization projects – good to have
Implement data quality, monitoring, alerting, security, governance, and compliance standards.
Collaborate with data architects and business teams to understand migration requirements and translate them into technical solutions.
Optimize data processing performance, cost, and scalability across AWS services.
Participate in CI/CD best practices and DevOps processes for data engineering workflows.

Mandatory Skills

Core Technologies

Apache Airflow (DAG development, workflow orchestration, scheduling, monitoring)
AWS Glue (ETL jobs, Glue Studio, Crawlers, Data Catalog, Spark-based transformations)
Snowflake (Data Warehousing, SnowSQL, Snowpipe, Streams & Tasks, Performance Tuning)

AWS Lambda and Step Functions (preferred)
Git experience (must-have)

Programming languages: Python, SQL, Spark (any 1 is must)

PySpark, Matillion, Shell scripting (good to have)

Experience Requirements

2+ years of intensive hands-on experience with AWS data services.
Proven experience migrating ETL platforms such as Matillion (or other ETL tools like DataStage) to AWS-native architectures (Good to have).
Strong experience developing workflows in Apache Airflow.
Extensive experience building and optimizing AWS Glue jobs.
Hands-on experience with Snowflake stored procedures (must-have), performance tuning (good-to-have).
Strong understanding of cloud architecture, scalability, deployments.

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Bangalore North, India

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