Senior Data Engineer
Atlanta, GA
- Pay
- Pay not listedSimilar roles state $127K–$190K
- Work setting
- Not stated
- Employment
- Not stated
Tools in this posting
- Python
- SQL
- AWS
- dbt
- PostgreSQL
- Redshift
- S3
- SageMaker
- Great Expectations
Source — Tool mentions in context
+ Build and optimize batch, incremental, event-driven, and near-real-time integrations across APIs, databases, files, ServiceNow, CMDB, observability, telemetry, and enterprise platforms. + Develop efficient, maintainable Python and SQL for data pipelines, orchestration, transformations, analytics layers, APIs, automation, and reusable engineering frameworks. + Establish trusted data products through automated controls for completeness, accuracy, freshness, reconciliation, monitoring, alerting, and exception handling.
+ Hands-on experience with Amazon S3, AWS Glue, Amazon Athena, Amazon Redshift, AWS Lambda, Amazon EventBridge, AWS Lake Formation, AWS IAM, and related data services. + Advanced proficiency in Python and SQL for ingestion, transformation, automation, analysis, data services, and performance optimization. + Experience designing scalable ETL and ELT pipelines integrating APIs, databases, structured files, semi-structured content, and cloud services.
+ Strong understanding of data governance, data classification, privacy, encryption, retention, identity, authorization, and secure handling of personal and sensitive information. + Experience with Amazon Redshift development concepts, including SQL optimization, workload performance, distribution and sort strategies, materialized views, stored procedures, access controls, and columnar formats such as Parquet. + Experience with Git, automated testing, CI/CD, Infrastructure as Code such as AWS CDK, or CloudFormation.
+ Lead independently across complex data initiatives, proactively identifying improvement opportunities, shaping solutions, and driving delivery from concept through production. + Design and modernize secure, scalable AWS data platforms, pipelines, Lakehouse patterns, and reusable data products aligned to enterprise architecture and governance standards. + Provide senior technical leadership for ingestion, transformation, modeling, metadata, quality, lineage, performance optimization, and production reliability.
+ 7+ years of IT experience, including 4+ years of hands-on experience designing, developing, and supporting enterprise data pipelines, data platforms, lakehouses, or analytical data solutions. + Demonstrated experience designing and implementing cloud-based data lake, data warehouse, or lakehouse architectures using AWS services. + Hands-on experience with Amazon S3, AWS Glue, Amazon Athena, Amazon Redshift, AWS Lambda, Amazon EventBridge, AWS Lake Formation, AWS IAM, and related data services.
+ Demonstrated experience designing and implementing cloud-based data lake, data warehouse, or lakehouse architectures using AWS services. + Hands-on experience with Amazon S3, AWS Glue, Amazon Athena, Amazon Redshift, AWS Lambda, Amazon EventBridge, AWS Lake Formation, AWS IAM, and related data services. + Advanced proficiency in Python and SQL for ingestion, transformation, automation, analysis, data services, and performance optimization.
+ Experience with Amazon Redshift development concepts, including SQL optimization, workload performance, distribution and sort strategies, materialized views, stored procedures, access controls, and columnar formats such as Parquet. + Experience with Git, automated testing, CI/CD, Infrastructure as Code such as AWS CDK, or CloudFormation. + Ability to communicate complex technical concepts, data limitations, architecture decisions, AI data requirements, and implementation plans to technical and business audiences.
What will give you a competitive edge (preferred qualifications) + AWS Certified Data Engineer, AWS Certified Solutions Architect, AWS Certified Machine Learning Engineer, or another relevant AWS certification. + Hands-on experience with Amazon SageMaker, Amazon Bedrock, Bedrock Knowledge Bases, Bedrock Agents or AgentCore, Amazon OpenSearch Service, foundation models, embedding models, and production generative AI architectures.
+ Experience implementing OAuth 2.0, OpenID Connect, identity federation, role-based access controls, and document- or row-level authorization for data and AI retrieval + Experience with automated data testing and quality frameworks such as AWS Glue Data Quality, Great Expectations, dbt tests, or comparable technologies + Demonstrated history of leading data modernization, cloud migration, operational intelligence, platform engineering, generative AI, or AI-enablement initiatives
+ Experience implementing enterprise RAG solutions, semantic search, hybrid retrieval, reranking, retrieval evaluation, grounding, citations, and AI-powered knowledge repositories. + Experience designing or integrating vector databases and vector-capable platforms such as Amazon OpenSearch Service, PostgreSQL with pgvector, or comparable technologies. + Experience with prompt engineering, model evaluation, guardrails, hallucination reduction, responsible AI controls, observability, and secure AI deployment patterns.
+ Establish trusted data products through automated controls for completeness, accuracy, freshness, reconciliation, monitoring, alerting, and exception handling. + Advance AI-ready architectures that prepare structured and unstructured data for Amazon SageMaker, Amazon Bedrock, LLMs, RAG, vector databases, semantic search, agents, and MCP-enabled solutions. + Design retrieval, embedding, indexing, enrichment, and context-management patterns that improve data usability, discoverability, and responsible AI outcomes.
+ Experience implementing metadata, lineage, cataloging, source-to-target mapping, data-quality testing, freshness monitoring, logging, alerting, and exception handling. + Working knowledge of Amazon SageMaker, Amazon Bedrock, foundation models, LLM application architectures, RAG, embeddings, vector search, semantic retrieval, and AI-ready data preparation. + Experience preparing structured and unstructured data through parsing, chunking, enrichment, embedding generation, indexing, access filtering, and context management.
+ AWS Certified Data Engineer, AWS Certified Solutions Architect, AWS Certified Machine Learning Engineer, or another relevant AWS certification. + Hands-on experience with Amazon SageMaker, Amazon Bedrock, Bedrock Knowledge Bases, Bedrock Agents or AgentCore, Amazon OpenSearch Service, foundation models, embedding models, and production generative AI architectures. + Experience implementing enterprise RAG solutions, semantic search, hybrid retrieval, reranking, retrieval evaluation, grounding, citations, and AI-powered knowledge repositories.
Posting record
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- Status in our records
- Live
- First seen by us
- Oct 29, 2026 · today
- Recorded sightings
- 1
- Employer says posted
- Oct 17, 2026
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- Pay
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- Location & working pattern
Atlanta, GA
+ Hands-on experience with Amazon SageMaker, Amazon Bedrock, Bedrock Knowledge Bases, Bedrock Agents or AgentCore, Amazon OpenSearch Service, foundation models, embedding models, and production generative AI architectures. + Experience implementing enterprise RAG solutions, semantic search, hybrid retrieval, reranking, retrieval evaluation, grounding, citations, and AI-powered knowledge repositories. + Experience designing or integrating vector databases and vector-capable platforms such as Amazon OpenSearch Service, PostgreSQL with pgvector, or comparable technologies.
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