Data Engineer
Summary
Builds and maintains scalable RAG pipelines and ETL/ELT workflows in AWS, using Python, Prefect, ElasticSearch, and NiFi to support federal AI and analytics workloads.
We are looking for a Data Engineer with strong hands‑on experience designing, developing, and managing large‑scale data workflows across structured and unstructured datasets. This role focuses heavily on building reliable RAG (Retrieval-Augmented Generation) pipelines, orchestrating ETL/ELT processes, and deploying scalable data systems in AWS.
Responsibilities
- Design, build, and maintain RAG pipelines, including document ingestion, indexing, embedding workflows, and model retrieval flows.
- Develop and manage structured and unstructured data pipelines supporting analytics, ML, and application workloads. Build and optimize ETL/ELT pipelines in AWS using services such as S3, Lambda, Step Functions, EMR, Glue, ECS/EKS, and IAM best practices. Implement and operate NiFi flows for high‑throughput, low‑latency data ingestion and transformation.
- Develop, orchestrate, and schedule workflows using Prefect, ensuring reliability, observability, and proper error handling. Implement indexing, search, and retrieval patterns using ElasticSearch, including schema design, cluster management, and query optimization.
- Collaborate closely with architecture, ML, and application teams to support scalable data solutions.
- Ensure data quality, lineage, governance, and security across all pipelines.
- Monitor system performance and troubleshoot issues across distributed data systems.
Qualifications
- Solid understanding of ETL/ELT processes and data modeling best practices.
- Hands‑on experience implementing workflows in Prefect (Prefect 2.0 preferred). In‑depth knowledge of ElasticSearch indexing, cluster management, and search optimization.
- Proficiency in Python and familiarity with common data libraries (Pandas, PySpark, requests, etc.).
Preferred Qualifications
- Strong experience with Apache NiFi for data flow management and real‑time ingestion.
- Experience building or maintaining RAG pipelines (e.g., vector databases, embeddings, document chunking strategies, retrieval optimization).
- Proven ability to manage structured and unstructured data pipelines at scale.
- Experience with AWS cloud services for data engineering.
- Strong version control and CI/CD experience.
- Experience with vector databases (OpenSearch, Pinecone, Weaviate, etc.)
- Familiarity with containerized workflows (Docker, Kubernetes)
- Experience supporting LLM or generative AI production systems
- Background in distributed systems, streaming platforms, or data mesh architectures
Clearance
- An active TS/SCI federal security clearance is required
As required by local law, Accenture Federal Services provides reasonable ranges of compensation for hired roles based on labor costs in the states of California, Colorado, Connecticut, Hawaii, Illinois, Maine, Maryland, Massachusetts, Minnesota, New Jersey, New York, Ohio, Vermont, Virginia, Washington, and the District of Columbia. The base pay range for this position in these locations is shown below. Compensation for roles at Accenture Federal Services varies depending on a wide array of factors, including but not limited to office location, role, skill set, and level of experience. Accenture Federal Services offers a wide variety of benefits. You can find more information on benefits here. We accept applications on an on-going basis and there is no fixed deadline to apply.
Skills
- AI
- Analytics
- AWS
- CI/CD
- Cloud
- Data Engineering
- Data Ingestion
- Data Modeling
- Data Pipelines
- Data Quality
- Distributed Systems
- Docker
- ECS
- EKS
- Elasticsearch
- ELT
- Embeddings
- ETL
- Generative AI
- IAM
- Kubernetes
- Lambda
- LLM
- Machine Learning
- NiFi
- Observability
- OpenSearch
- pandas
- Pinecone
- Prefect
- PySpark
- Python
- RAG
- S3
- TypeScript
- Vector Databases
- Version Control
- Weaviate
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