Pay
In line with similar jobs
$103K–$196K against $127K–$190K, the middle half of what similar US postings state.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
Washington, DC
Live, first seen by us 28 days agoThe pay range for 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 is: $103,200—$196,400 USD What We BelieveRead the full posting
Work setup in the employer’s posting: RemoteFrom the employer’s posting
Must be a US Citizen with ability to obtain a Public Trust clearance
Build and optimize batch and streaming data pipelines for analytics and AI
Ingest structured and unstructured data from APIs, databases, and streams
Transform and standardize data using Dataflow, Dataproc Spark, BigQuery SQL, and Python
Build data quality checks, schema validation, and automated pipeline tests
Monitor pipeline health and troubleshoot failures and latency issues
Build, maintain, and optimize batch and streaming data pipelines to support analytics and AI workloads.
Ingest structured, semi structured, and unstructured datasets from APIs, databases, SaaS systems, streaming feeds, and file based sources.
Transform, clean, enrich, and standardize data using Dataflow (Apache Beam), Dataproc (Spark), BigQuery SQL, and Python.
Implement data quality checks, schema validation, and automated testing within pipelines.
Troubleshoot pipeline failures, latency issues, and data quality gaps.
Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or related technical field.
3–6+ years of hands on experience in data engineering or a similar technical field.
Experience developing and implementing technical standards for cloud and on prem environments.
Proven experience building production data pipelines on cloud platforms, preferably GCP.
Hands on experience with BigQuery, GCS, Dataflow (Apache Beam), Dataproc (Spark), and Pub/Sub.
Experience preparing ML ready datasets for model training.
Master’s degree in a technical field.
Knowledge of regulated environments such as FedRAMP, HIPAA, PCI, NIST 800 53, and CIS benchmarks.
Experience with Vertex AI workflows or comparable ML platforms.
Familiarity with Dataplex, data governance frameworks, and metadata management.
- Must be a U.S. Citizen with ability to obtain a Public Trust clearance. - Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or related technical field. - 3–6+ years of hands on experience in data engineering or a similar technical field.
Preferred Experience - Master’s degree in a technical field. - Previous experience in Federal Government environments.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
In line with similar jobs
$103K–$196K against $127K–$190K, the middle half of what similar US postings state.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
Day 28
Similar jobs usually come down between day 5 and day 54.
From 10,736 similar postings that came down. Coming down is not always a hire.
89 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Ingest structured, semi structured, and unstructured datasets from APIs, databases, SaaS systems, streaming feeds, and file based sources. - Transform, clean, enrich, and standardize data using Dataflow (Apache Beam), Dataproc (Spark), BigQuery SQL, and Python. - Deliver high quality curated datasets into BigQuery for analytics, reporting, and machine learning training.
Engineering & Collaboration - Write efficient, maintainable Python, SQL, Beam, and Spark code. - Manage ingestion flows using Pub/Sub, GCS, APIs, Datastream, and database connectors.
- Experience preparing ML ready datasets for model training. - Strong background in SQL, Python, distributed data processing, and data modeling. - Experience with governance, security, and compliance frameworks including IAM, encryption, data masking, and auditing.
- Transform, clean, enrich, and standardize data using Dataflow (Apache Beam), Dataproc (Spark), BigQuery SQL, and Python. - Deliver high quality curated datasets into BigQuery for analytics, reporting, and machine learning training. - Build and maintain ML ready feature pipelines supporting Data Scientists and ML Engineers.
- Manage ingestion flows using Pub/Sub, GCS, APIs, Datastream, and database connectors. - Optimize BigQuery tables, partitions, clustering, materialized views, and query performance. - Implement and maintain DAGs with Cloud Composer (Airflow).
- Proven experience building production data pipelines on cloud platforms, preferably GCP. - Hands on experience with BigQuery, GCS, Dataflow (Apache Beam), Dataproc (Spark), and Pub/Sub. - Experience preparing ML ready datasets for model training.
- Knowledge in ML enablement, feature stores, and ML pipeline patterns. - Experience with data quality and testing frameworks such as Great Expectations or dbt tests. 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.
- Experience developing and implementing technical standards for cloud and on prem environments. - Proven experience building production data pipelines on cloud platforms, preferably GCP. - Hands on experience with BigQuery, GCS, Dataflow (Apache Beam), Dataproc (Spark), and Pub/Sub.
- Familiarity with the following tool categories (VAEC Operational Tools): - Google Cloud Security tools - Google Cloud Monitoring & Logging tools
- Google Cloud Security tools - Google Cloud Monitoring & Logging tools - Google Cloud Networking
- Google Cloud Monitoring & Logging tools - Google Cloud Networking - Google Storage services
- Write efficient, maintainable Python, SQL, Beam, and Spark code. - Manage ingestion flows using Pub/Sub, GCS, APIs, Datastream, and database connectors. - Optimize BigQuery tables, partitions, clustering, materialized views, and query performance.
- Familiarity with Dataplex, data governance frameworks, and metadata management. - Experience with Apache Kafka or other streaming technologies. - Experience with Datastream for change data capture (CDC).
- Strong communication skills and the ability to convey complex data concepts clearly. - Experience with BI tools such as Looker or Looker Studio. - Experience with third party tools such as Armis, BigFix, CrowdStrike, Tenable Nessus, Turbot, ServiceNow, Dynatrace, Splunk, and more.
- Collaborate with Data Architects, Data Scientists, ML Engineers, and business stakeholders. - Develop and maintain Infrastructure as Code using Terraform and CI/CD deployment pipelines. Required Qualifications
- Experience with third party tools such as Armis, BigFix, CrowdStrike, Tenable Nessus, Turbot, ServiceNow, Dynatrace, Splunk, and more. - Hands on experience with DevOps tools and methodologies, including Ansible, GitHub, Jira, Terraform, CI/CD, and cloud migration tools. - Knowledge in ML enablement, feature stores, and ML pipeline patterns.
- Optimize BigQuery tables, partitions, clustering, materialized views, and query performance. - Implement and maintain DAGs with Cloud Composer (Airflow). - Troubleshoot pipeline failures, latency issues, and data quality gaps.
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Yes. We’ve followed 10 other Data Engineer jobs there since May 2026. 3 came down, usually after about 27 days. 7 are still up.
Large employers often post one title for several teams, so these can be separate openings.
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Key Responsibilities
Core Data & AI Pipeline Development
Data Quality, Governance & Operations
Engineering & Collaboration
Required Qualifications
Preferred Experience
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.
Selected passages from the saved posting. Check the full description for conditions and exceptions.
The pay range for 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 is: $103,200—$196,400 USD What We Believe
Washington, DC
Working pattern and location restrictions need checking in the full posting.
Required Qualifications - Must be a U.S. Citizen with ability to obtain a Public Trust clearance. - Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or related technical field.
Equal Employment Opportunity Statement We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities. For details, view a copy of the Accenture Federal Services Equal Opportunity Policy Statement. Accenture Federal Services is an Equal Employment Opportunity employer. Additionally, as an Affirmative Action Employer for Veterans and Individuals with Disabilities, Accenture Federal Services is committed to providing veteran employment opportunities to our service men and women.
More relevant text appears in the full description.