Senior Data Engineer

Reveal Global Consulting LLC•Fulton, MD

9d•Hybrid

About The Position

The candidate must be a hands-on Data Engineer with the ability to lead the work, engage directly with the client, understand requirements, and design and develop effective solutions. The role also requires Oracle/PL/SQL development and database-modernization experience, including migration from Oracle to PostgreSQL in the cloud. Strong communication and the ability to collaborate effectively are essential.

Requirements

10 + years of experience
Hands-on Data Engineer with the ability to lead work
Ability to engage directly with the client
Ability to understand requirements
Ability to design and develop effective solutions
Oracle/PL/SQL development experience
Database-modernization experience
Migration from Oracle to PostgreSQL in the cloud experience
Strong communication skills
Ability to collaborate effectively
Python
PySpark
SQL
AWS DMS
AWS Glue
AWS Step Functions
Amazon S3
Amazon Redshift
Amazon RDS
Amazon EMR
AWS IAM
AWS Lambda
Snowflake
AWS CloudWatch
AWS CloudTrail
AWS CloudFormation
Terraform
GDPR
HIPAA

Responsibilities

Design, implement, and manage robust data pipelines using Python, PySpark, and SQL to efficiently extract, transform, and load data from diverse batch and streaming sources.
Demonstrate expertise in core AWS services such as AWS DMS, AWS Glue, AWS Step Functions, Amazon S3, Amazon Redshift, Amazon RDS, Amazon EMR, AWS IAM, and AWS Lambda, and apply them to build scalable and reliable data solutions.
Develop and maintain efficient data models to support analytical and reporting needs.
Develop, maintain, troubleshoot, and optimize Oracle database objects and PL/SQL components, including stored procedures, packages, functions, triggers, views, and complex SQL queries.
Assess Oracle schemas and PL/SQL code, identify compatibility gaps, convert database objects and SQL logic to PostgreSQL, and support cloud migration planning, data validation, testing, performance tuning, cutover, and post-migration stabilization.
Administer databases using AWS services such as Amazon RDS or Amazon Redshift, with a focus on schema design, performance optimization, and monitoring.
Use Amazon Redshift or Snowflake to create high-performing analytical databases that support data-driven decision-making.
Implement industry best practices for ETL processes, including data validation, error handling, and data-quality checks.
Optimize query performance through continuous database tuning and effective use of AWS scalability capabilities.
Establish robust monitoring and logging mechanisms using AWS CloudWatch, AWS CloudTrail, or comparable tools to ensure pipeline reliability.
Follow security best practices and applicable compliance standards, tailoring solutions to meet GDPR, HIPAA, or other regulatory requirements.
Automate deployment and scaling of data pipelines using infrastructure-as-code tools such as AWS CloudFormation and Terraform.
Collaborate closely with cross-functional teams, including data scientists, analysts, and other stakeholders, to understand their data needs and provide effective solutions.
Stay current with developments in AWS services and data-engineering methodologies and apply new insights to enhance the data infrastructure.