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Accenture

Advises businesses and runs their technology

Analistas e Consultores: AI - Data Engineering & Media Growth

Data EngineeringConfirmed live in the last 24 hours
Full-timeRemote in Brazil

About the Job

Requirements

  • For Media Growth: knowledge of pre- and post-click media metrics such as reach, frequency, CPM, CTR, VTR, CPA, and ROAS.
  • For Media Growth: experience with media platforms such as Meta Ads, Google Ads, DV360/CM360, TikTok Ads, Amazon Ads, and Mercado Ads.
  • For Media Growth: knowledge of GA4 or Adobe Analytics.
  • For Media Growth: intermediate SQL, preferably BigQuery.
  • For Media Growth: advanced Excel or Google Sheets skills.
  • For Media Growth: Power BI, data visualization, and storytelling skills.
  • For Data Engineering: Python and advanced SQL.
  • For Data Engineering: REST APIs, OAuth, and webhooks.
  • For Data Engineering: media or analytics platform APIs, such as Meta API, Google Ads API, and GA4 Data API.
  • For Data Engineering: cloud experience, preferably GCP, including Cloud Functions or Cloud Run, Pub/Sub, Cloud Scheduler, and BigQuery.
  • For Data Engineering: dbt for transformation and modeling.
  • For Data Engineering: Airflow/Composer or Dagster.
  • For Data Engineering: dimensional modeling.
  • For Data Engineering: Git, code review, and CI/CD.
  • For Data Engineering: GA4 or Adobe Analytics and media platforms such as Meta and Google Ads.
  • For Data Engineering: Power BI.
  • For Data Engineering: knowledge of the digital media funnel, including conversions, attribution, audiences, and campaign metrics.
  • For AI Engineering: solid software engineering in Python, including typing, Pydantic, async, automated testing, Git, and CI/CD.
  • For AI Engineering: APIs with FastAPI and containers.
  • For AI Engineering: SQL and familiarity with data warehouses such as BigQuery, Snowflake, or Databricks.
  • For AI Engineering: experience with production LLM applications, including tool calling, structured outputs, and context engineering.
  • For AI Engineering: agent frameworks such as LangGraph, Google ADK, OpenAI Agents SDK, CrewAI, or similar.
  • For AI Engineering: cloud AI platforms such as Vertex AI/Gemini Enterprise, Amazon Bedrock, or Azure AI Foundry.
  • For AI Engineering: LLM evaluation and observability using tools such as Langfuse, LangSmith, Arize Phoenix, or OpenTelemetry GenAI.
  • For AI Engineering: vector databases and hybrid search, such as pgvector, Vertex AI Vector Search, and Elasticsearch/OpenSearch.

Responsibilities

  • For Media Growth: analyze creative saturation, reach and frequency, auction efficiency, and delivery.
  • For Media Growth: analyze creatives by format, message, and duration.
  • For Media Growth: assess media quality, including viewability, brand safety, and invalid traffic (IVT).
  • For Media Growth: recommend budget distribution across channels, campaigns, and audiences.
  • For Media Growth: generate and validate campaign datasets by joining media-platform data with GA4 or Adobe Analytics.
  • For Media Growth: conduct journey and attribution analysis beyond last click, and analyze the performance of landing pages, microsites, and apps.
  • For Media Growth: design and analyze incrementality and brand-lift tests, such as A/B, geo-lift, and conversion-lift tests.
  • For Media Growth: build and evolve dashboards in Power BI and Looker Studio.
  • For Media Growth: use generative AI to support analysis, SQL review, data exploration, creative classification, anomaly detection, and generation of performance recommendations.
  • For Data Engineering: build and maintain media data-ingestion pipelines, including data from Meta, Google Ads, DV360, and TikTok, as well as analytics and CRM data.
  • For Data Engineering: model data in bronze, silver, and gold layers with dbt, creating unified views of media, customer journeys, and customers.
  • For Data Engineering: ensure data quality and observability through automated tests, data contracts, monitoring, and alerts.
  • For Data Engineering: structure the semantic layer and business metrics, such as ROAS, CAC, LTV, and conversions.
  • For Data Engineering: automate media operations through APIs, including bulk creation and updates of campaigns, audiences, and creatives.
  • For Data Engineering: incorporate LLMs and agents into operational workflows, including copy variations, creative and search-term classification, and automated performance summaries.
  • For AI Engineering: design, build, and deploy agentic systems, including agents with tool calling and multi-agent orchestration.
  • For AI Engineering: apply context engineering by defining the data, tools, memory, and instructions used by the model.
  • For AI Engineering: work with hybrid search, reranking, and GraphRAG.
  • For AI Engineering: connect agents to data and systems through MCP (Model Context Protocol).
  • For AI Engineering: create and maintain evaluation pipelines, including golden datasets, LLM-as-a-judge, and regression tests.
  • For AI Engineering: operate LLMOps/AgentOps, including prompt and agent versioning, tracing, observability, cost and latency controls, model routing, and caching.
  • For AI Engineering: implement guardrails and security, including prompt-injection protection, structured-output validation, PII/LGPD handling, and human-in-the-loop processes.
  • For AI Engineering: work with Data Engineering to ensure data is prepared for AI, including the semantic layer, metadata, quality, and governance.
  • For AI Engineering: develop using coding assistants such as GitHub Copilot, Claude Code, and Cursor, applying spec-driven development, automated testing, and code review.

Desired Qualifications

  • For Media Growth: generative AI applied to data analysis.
  • For Media Growth: Python for data analysis.
  • For Media Growth: retail media, including Mercado Ads and Amazon Ads.
  • For Media Growth: connected TV (CTV).
  • For Media Growth: English.
  • For Data Engineering: Snowflake or Databricks.
  • For Data Engineering: Fivetran, Airbyte, or dlt.
  • For Data Engineering: Terraform.
  • For Data Engineering: FinOps, partitioning, and clustering.
  • For Data Engineering: Docker.
  • For Data Engineering: English.
  • For AI Engineering: security in LLM applications and responsible AI.
  • For AI Engineering: developing MCP servers.
  • For AI Engineering: open-weight models and optimized inference, such as vLLM, Ollama, and quantization.
  • For AI Engineering: marketing data, including GA4, Adobe Analytics, Meta, Google Ads, and CDPs.
  • For AI Engineering: classical machine learning and statistics.
  • For AI Engineering: English.

About the Company

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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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