Senior/ Lead AWS Data Engineer

We are

At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron’s progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 14,500+, and has 58 offices in 21 countries within key global markets.

Our Challenge:

We are seeking an experienced AWS Data Engineer to support both legacy enterprise data pipelines and upcoming modern data lake initiatives. The role requires strong hands-on expertise in SQL, Python, ETL/data integration , and the ability to work across technologies such as Liquibase, SSIS, TFS, AutoSys, Hive, Dremio, PySpark, Airflow, and Data Lake architecture .

Additional Information*

The base salary for this position will vary based on geography and other factors.

In accordance with law, the base salary for this role if filled within Piscataway, NJ is $115K - $125K/ year & benefits (see below).

Responsibilities:

Legacy Data Pipeline Support and Enhancement

• Quickly understand and support existing legacy data integration and ETL pipelines.
• Work hands-on with SQL, Liquibase, SSIS, TFS, and AutoSys to maintain and enhance current data workflows.
• Analyze existing data movement processes, database scripts, and scheduling dependencies.
• Troubleshoot pipeline failures, performance bottlenecks, and data quality issues in legacy systems.
• Perform root cause analysis and implement sustainable fixes for recurring issues.
• Support change deployments, release management, and version-controlled database updates.

Modern Data Lake Pipeline Development

• Design, build, and optimize scalable data pipelines for a modern data lake environment.
• Develop and maintain batch and/or scheduled data ingestion, transformation, and processing frameworks using Python, PySpark, Hive, Dremio, and Airflow.
• Contribute to the architecture and implementation of cloud-based data lake solutions on AWS.
• Build reusable, maintainable, and testable data engineering components for ingestion, transformation, and serving layers.
• Support the migration of data processes from legacy systems to modern data lake platforms where applicable.
• Ensure pipeline reliability, observability, scalability, and performance across the new platform.

Data Engineering and Integration

• Build robust ETL/ELT workflows for structured and semi-structured datasets.
• Write complex SQL queries, stored procedures, scripts, and data transformation logic.
• Work with upstream and downstream application teams to understand integration requirements and data dependencies.
• Validate and reconcile data across source systems, staging layers, and target environments.
• Ensure high standards of data integrity, consistency, and availability.

Requirements:

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
• 10+ years of experience in data engineering, ETL development, or data platform engineering.
• Legacy Pipeline (Immediate ramp-up): SQL, Liquibase, SSIS, TFS, AutoSys
• New Data Lake Pipeline (Upcoming project): Strong hands-on experience in Hive, Dremio, Python, PySpark, Airflow, Data Lake Architecture
• Strong understanding of ETL/ELT design, data integration patterns, and pipeline orchestration.
• Experience working in AWS-based data environments.
• Experience with AWS data services such as S3, Glue, Lambda, EMR, Athena, Redshift, Step Functions, or CloudWatch .
• Familiarity with DevOps and CI/CD practices for data engineering workflows.
• Experience with schema evolution, metadata management, and data quality controls.
• Exposure to enterprise scheduling, release processes, and production support models.
• Ability to understand and navigate existing codebases in Java and C# .

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