Data Engineer

Accenture Federal Services is a technology company dedicated to helping the US federal government strengthen national security, public safety, civilian, and health missions through technology and ingenuity. The Data Engineer will build and optimize cloud-based batch and streaming data pipelines, prepare curated and ML-ready datasets, implement data quality and governance practices, and collaborate with architects, data scientists, ML engineers, and stakeholders. The role also involves pipeline operations, infrastructure as code, CI/CD, troubleshooting, and technical collaboration.

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

Requirements:

• 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