Note: The job is a remote job and is open to candidates in USA. Accenture Federal Services is hiring a Data Engineer to build and operate cloud data pipelines supporting analytics and AI workloads for federal missions. The role focuses on delivering curated, ML-ready data while ensuring data quality, governance, security, and collaboration across engineering and data teams.
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
Skills
- 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
- 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
Benefits
- Hands-on experience, certifications, and industry training
- Remote work
Company Overview