AWS Data Engineer

Facctum; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Facctum in Bengaluru, India, seeks an experienced AWS Data Engineer to lead a migration from Matillion ETL to a modern AWS-native data platform. You will design scalable pipelines using Apache Airflow, AWS Glue and Snowflake, collaborating with stakeholders to migrate workloads.

The role requires 2+ years of AWS data services experience, strong Python/SQL/Spark skills, and a proven ability to build reliable ETL/ELT components with data quality, governance and cost optimization.

Qualifications

• 2+ years of hands-on experience with AWS data services.
• Proven migration of ETL platforms like Matillion to AWS-native.
• Strong Airflow workflow development experience.
• Extensive AWS Glue job development and optimization.
• Snowflake stored procedures experience and performance tuning.

Responsibilities

• Develop modules for migrating Matillion jobs to AWS-native solutions.
• Design and implement scalable data pipelines on AWS.
• Build ingestion, transformation, and orchestration frameworks.
• Collaborate with data architects and business teams on migration requirements.
• Apply data quality, security, governance, and CI/CD practices.
• Optimize processing performance, cost, and scalability on AWS.
• Participate in DevOps processes for data engineering workflows.

Skills

Apache Airflow

AWS Glue

Snowflake

Python

SQL

Spark

Matillion

Shell scripting

PySpark

Tools

Git

Job description

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.