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.
Skills Required
- At least 2 years of hands-on experience with AWS data services
- Apache Airflow experience, including DAG development, workflow orchestration, scheduling, and monitoring
- AWS Glue experience, including ETL jobs, Glue Studio, Crawlers, Data Catalog, and Spark-based transformations
- Snowflake data warehousing experience
- Snowflake stored procedures experience
- Data modeling and data warehousing concepts
- Git experience
- Proficiency in at least one of Python, SQL, or Spark
- Strong understanding of cloud architecture, scalability, and deployments
- AWS Lambda and Step Functions experience
- Experience migrating Matillion or other ETL platforms to AWS-native architectures
- Snowflake performance tuning experience
- PySpark, Matillion, or shell scripting experience
What We Do
Facctum is a compliance-technology company that helps financial institutions manage financial-crime risk. Its software provides watchlist management, customer and payment screening, transaction monitoring, alert adjudication, and related due-diligence capabilities. Using machine learning, dynamic screening, and data-centric tooling, Facctum aims to improve detection, regulatory compliance, operational efficiency, and customer experience across complex financial-services environments through configurable, scalable platforms designed for regulated organizations worldwide.
