Senior Data Engineer

Great Waters Federal is part of the Seneca Nation Group (SNG) portfolio of companies. SNG is Seneca Holdings' federal government contracting business that meets mission-critical needs of federal civilian, defense, and intelligence community customers. Our portfolio comprises multiple subsidiaries that participate in the Small Business Administration 8(a) program. To learn more about SNG, visit the website and follow us on LinkedIn.

Our team of talented individuals is what makes us successful. To support our team, we provide a balanced mix of benefits and programs. Your total rewards package includes competitive pay, benefits, and perks, flexible work-life balance, professional development opportunities, and performance and recognition programs. We offer a comprehensive benefits package that includes medical, dental, vision, life, and disability, voluntary benefit programs (critical illness, hospital, and accident), health savings and flexible spending accounts, and retirement 401K plan. One of our fundamental principles is to offer competitive health and welfare benefits to our team members, providing coverage and care for you and your family. Full-time employees working at least 30 hours a week on a regular basis are eligible to participate in our benefits and paid leave programs. We pride ourselves on our collaborative work environment and culture, which embraces our mission of providing financial and non-financial benefits back to the members of the Seneca Nation.

We are seeking an experienced Data Engineer to design, build, and maintain scalable data pipelines within a Databricks E2 environment hosted on AWS and governed by FISMA High and multi-tenant security and compliance standards.

The ideal candidate has strong hands-on experience with Databricks, Apache Spark, and the broader AWS data ecosystem. This individual should be comfortable working across the full data lifecycle—from ingestion and transformation to the delivery of analytics-ready datasets.

Key Responsibilities

• Analyze and collect data from various sources, including relational databases, APIs, external data providers, and real-time streaming sources.
• Design and implement efficient, scalable data pipelines to cleanse, transform, and aggregate data for downstream reporting and analytics.
• Build and maintain Databricks solutions using medallion architecture, including Bronze, Silver, and Gold layers, to create structured, reliable, and progressively refined datasets.
• Develop source-to-target mapping documentation for all data pipelines and conduct thorough unit testing to validate data accuracy and pipeline logic.
• Write advanced SQL to create aggregations across complex datasets and analyze data for anomalies, quality issues, and inconsistencies.
• Implement real-time and near-real-time data ingestion using AWS Database Migration Service (DMS) and other AWS-native services.
• Develop data-processing logic using Python and/or R, leveraging Spark and related packages such as PySpark and Pandas for large-scale data transformation.
• Manage source code, versioning, and deployments using GitLab, and support automated build and release processes through CI/CD pipelines.
• Collaborate with cross-functional teams in an Agile project environment, participating in sprint planning, stand-ups, and iterative delivery.
• Ensure that data engineering practices align with FISMA High and multi-tenant security, governance, and compliance requirements.
• Leverage AI automation tools to support data engineering pipeline development, testing, and validation and to accelerate delivery.

Required Qualifications

• Proven, hands-on experience as a Data Engineer or Databricks Developer building production-grade data pipelines.
• Strong working knowledge of Databricks on AWS, including E2 architecture, cluster configuration, job orchestration, and workspace management.
• Hands-on experience with Apache Spark, PySpark, and Pandas for large-scale, distributed data processing.
• Solid programming skills in Python; working knowledge of R is a plus.
• Experience with Databricks Auto Loader for scalable, incremental file ingestion.
• Practical experience with AWS Database Migration Service (DMS) for change data capture and real-time data replication.
• Strong experience with Delta Lake and Delta tables, including schema evolution, time travel, and optimization techniques such as OPTIMIZE, Z-ORDER, and VACUUM.
• Experience with Amazon RDS and other relational database sources used in data extraction and integration workflows.
• Advanced SQL skills, including complex joins, window functions, aggregations, and performance tuning.
• Solid understanding of medallion architecture and modern data lakehouse design principles.
• Experience with GitLab and CI/CD pipelines for automated testing, build, and deployment of data engineering code.
• Experience working in Agile/Scrum project environments.

Preferred Skills

• Broader knowledge of AWS services beyond DMS and RDS, including S3, Glue, Lambda, Step Functions, CloudWatch, and IAM.
• Experience operating within FISMA High, FedRAMP, or other regulated, multi-tenant compliance environments.
• Familiarity with data quality frameworks, anomaly detection techniques, and automated data validation.
• Exposure to infrastructure-as-code tools such as Terraform or CloudFormation for provisioning data platform resources.
• Knowledge of streaming technologies such as Kafka, Kinesis, or Spark Structured Streaming.