Data Engineer

Accenture Federal Services•Washington, DC

1d•$103,200 - $196,400

About The Position

At Accenture Federal Services, our purpose is to make the US federal government stronger and safer, and to improve the lives of its citizens through technology and ingenuity. We are a community of over 13,000 professionals dedicated to serving clients across defense, national security, public safety, civilian, and military health organizations. As a technology company within the global Accenture network, we are recognized as a Glassdoor Top 100 Best Place to Work. We foster a collaborative and supportive environment where employees are empowered to grow, learn, and thrive through hands-on experience, certifications, and industry training. Join us to drive meaningful, lasting change that advances government missions.

Requirements

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.
Minimum three years of experience leading technical teams to achieve outcomes.
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.
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.
Familiarity with the following tool categories (VAEC Operational Tools): Google Cloud Security tools, Google Cloud Monitoring & Logging tools, Google Cloud Networking, Google Storage services

Nice To Haves

Master’s degree in a technical field.
Previous experience in Federal Government environments.
Knowledge of regulated environments such as FedRAMP, HIPAA, PCI, NIST 800 53, and CIS benchmarks.
Security certifications such as CISSP or CCSP.
Experience with Vertex AI workflows or comparable ML platforms.
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).
Knowledge of regulated industries such as public sector, healthcare, or finance.
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.
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.
Experience with data quality and testing frameworks such as Great Expectations or dbt tests.

Responsibilities

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.
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.
Implement data quality checks, schema validation, and automated testing within pipelines.
Monitor pipeline health and apply observability best practices using Cloud Monitoring and Cloud Logging.
Apply governance, security, and compliance standards including IAM roles, encryption, data masking, and auditing.
Enforce schema evolution policies, metadata management, and lineage tracking using Dataplex/Data Catalog.
Maintain documentation for datasets, transformations, pipeline logic, and operational procedures.
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.
Implement and maintain DAGs with Cloud Composer (Airflow).
Troubleshoot pipeline failures, latency issues, and data quality gaps.
Participate in code reviews, architectural discussions, and agile sprint ceremonies.
Collaborate with Data Architects, Data Scientists, ML Engineers, and business stakeholders.
Develop and maintain Infrastructure as Code using Terraform and CI/CD deployment pipelines.