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Accenture

Advises businesses and runs their technology

Data Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeWashington, DC, USA$103.2k - $196.4kIn-person

About the Job

Requirements

  • Must be a U.S. citizen and able to obtain a Public Trust clearance.
  • A bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or a related technical field is required.
  • Three to six or more years of hands-on experience in data engineering or a similar technical field is required.
  • At least three years of experience leading technical teams to achieve outcomes is required.
  • Experience developing and implementing technical standards for cloud and on-premises environments is required.
  • Proven experience building production data pipelines on cloud platforms, preferably Google Cloud Platform, is required.
  • Hands-on experience with BigQuery, Google Cloud Storage, Dataflow (Apache Beam), Dataproc (Spark), and Pub/Sub is required.
  • Experience preparing machine-learning-ready datasets for model training is required.
  • A strong background in SQL, Python, distributed data processing, and data modeling is required.
  • Experience with governance, security, and compliance frameworks, including IAM, encryption, data masking, and auditing, is required.
  • Familiarity with Google Cloud Security tools, Google Cloud Monitoring and Logging tools, Google Cloud Networking, and Google Storage services is required.

Responsibilities

  • Build, maintain, and optimize batch and streaming data pipelines to support analytics and artificial intelligence 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 machine-learning-ready feature pipelines supporting data scientists and machine learning 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 and 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 directed acyclic graphs (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, machine learning engineers, and business stakeholders.
  • Develop and maintain infrastructure as code using Terraform and continuous integration/continuous delivery (CI/CD) deployment pipelines.

Desired Qualifications

  • A master's degree in a technical field is preferred.
  • Previous experience in federal government environments is preferred.
  • Knowledge of regulated environments such as FedRAMP, HIPAA, PCI, NIST 800-53, and CIS benchmarks is preferred.
  • Security certifications such as CISSP or CCSP are preferred.
  • Experience with Vertex AI workflows or comparable machine learning platforms is preferred.
  • Familiarity with Dataplex, data governance frameworks, and metadata management is preferred.
  • Experience with Apache Kafka or other streaming technologies is preferred.
  • Experience with Datastream for change data capture (CDC) is preferred.
  • Knowledge of regulated industries such as the public sector, healthcare, or finance is preferred.
  • Experience with business intelligence tools such as Looker or Looker Studio is preferred.
  • Experience with third-party tools such as Armis, BigFix, CrowdStrike, Tenable Nessus, Turbot, ServiceNow, Dynatrace, and Splunk is preferred.
  • Hands-on experience with DevOps tools and methodologies, including Ansible, GitHub, Jira, Terraform, CI/CD, and cloud migration tools, is preferred.
  • Knowledge of machine learning enablement, feature stores, and machine learning pipeline patterns is preferred.
  • Experience with data quality and testing frameworks such as Great Expectations or dbt tests is preferred.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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