Data Engineer

Overview

Accenture helps the world's leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed for organizations across industries. With ~786,000 employees worldwide, it serves approximately 9,000 clients in 120+ countries across 40+ industries transform their operations through technology and human ingenuity. Key service lines include Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology.

Job Title: Data Engineer
Location: Bay Area preferred, open to Seattle, New York, San Antonio, Austin
Duration: 12 Months
Job Type: Temporary Assignment
Work Type: Remote

Summary

• The Data Engineer owns the data lifecycle end to end at client: from a raw log line to a trusted dashboard number. You will develop, evaluate, test and maintain data architectures and pipelines on client's internal stack, keeping data correct, timely, secure and affordable.
• You will execute the plans, policies and practices that control, protect, deliver and enhance the value of the organization's data assets, and use client's AI assistants to work faster across every stage.

Key responsibilities

• Manage data engineering projects through the full cycle, from requirements to production support
• Identify and shape business initiatives from a data engineering perspective, working with client Product Managers and data scientists
• Design, build, test and maintain highly scalable data management systems and production pipelines (Python, SQL, Spark, Presto)
• Design, implement, automate and maintain large-scale enterprise ETL processes across capture, move, refine, store and serve layers
• Orchestrate pipelines with scheduling, retries, backfills and SLAs; ship changes through code review and CI/CD
• Ensure systems meet business requirements, client privacy policy and data governance standards, including access control and retention
• Monitor data quality, observability, cost and reliability at every layer; participate in 24/7 on-call rotations and SEV investigations
• Build high-performance algorithms, prototypes, predictive models and proofs of concept, including AI agents that automate operational work
• Build datasets, semantic layers and dashboards that give stakeholders self-service access to trusted metrics

Required technical skills

• Core languages and frameworks: Python, SQL, Apache Spark, Airflow, PrestoDB,
• Bash/shell scripting. Candidates should have hands-on experience with the client tools below for each stage of the lifecycle.

Lifecycle stage Capability Client tools Capture Logging, ingestion, scraping Falco, Logview, Scribe, Hive ETL, Scrapy, Playwright Move Pub/sub and streaming Scribe event streams, Scuba Refine Enriching, cleansing, aggregation Daiquery, Presto / Onyx, Hive, Confucius Store & secure Storage and access control Hive, Manifold, XDB, TAO, Everstore, AMP, Access Gate, CAT, OIDC Serve Semantic layer and visualization IData, Unidash, Vega, Nest Orchestration Scheduling, retries, backfills, SLAs, CI/CD Dataswarm, Sandcastle Configuration Feature flags, rollouts, experiments Configerator, Sitevars, JustKnobs, Gatekeeper Observability Monitoring, alerting, errors, on call ODS, Scuba, ErrorTool, Oncall Hub Development Source control, code review, IDE Sapling, Phabricator, Diffs, CodeHub, Paste, EdenFS, VS Code @ client Investigation Entity debugging, data discovery ID Tool, IData, Intern Search

AI tooling

Candidates are expected to use AI assistants daily and to build agents that remove manual work.

Tool Expected use Metamate Knowledge retrieval, document summaries, custom commands for repetitive tasks Confucius Agents for data collection, data quality monitoring, anomaly detection and reporting Claude (Opus, Sonnet) Pipeline code and code review, workflow scripting, documentation, compliance support Analytics Agent Finding data, analysing it and answering business questions

Professional skills and qualifications

• 5 10 years of data engineering experience, with production pipelines at petabyte scale
• Bachelor's degree in Computer Science, Engineering or a related field, or equivalent experience
• Ability to wo