🔥 Accenture is hiring for a Data Engineer – Data Eng, Mgmt & Governance Senior Analyst in Gurugram. This is not a conventional data engineering position—the role combines AWS AI Services, Agentic AI, GenAI engineering, data pipelines, AI-agent orchestration, memory, observability, guardrails and evaluation. Candidates with hands-on experience in modern AI-agent frameworks can position themselves strongly for this opportunity.
📌 Job Details
💼 Position: Data Engineer – Data Eng, Mgmt & Governance Senior Analyst
🏢 Company: Accenture
📍 Location: Gurugram, Haryana
⏳ Experience: 2–5 years
🎓 Education: 15 years of full-time education
🆔 Job No.: ATCI-5692862-S2063517
🧠 Required Skill: AWS AI Services
🤖 Specialization: Agentic AI / GenAI Engineering
📊 Employment: Full-time
🏢 About Accenture
🌎 Accenture is a global technology and professional services company focused on combining technology with human ingenuity to help organizations transform their businesses. The company works across areas including cloud, data, AI, software engineering, cybersecurity and digital transformation. Accenture states that more than 775,000 people work across its global organization. The company also emphasizes continuous learning, certifications and opportunities to work across different technologies and industries.
🤖 What Will You Work On?
🔹 Design and maintain data solutions for data generation, collection and processing.
🔹 Build reliable data pipelines and ETL workflows for moving data across systems.
🔹 Work with AWS AI Services, particularly the Amazon Bedrock ecosystem.
🔹 Develop AI agents using Strands, LangGraph or CrewAI.
🔹 Build and author MCP servers and tools that allow agents to interact with external systems.
🔹 Work with A2A protocols, agent registries and cross-agent state for multi-agent architectures.
🔹 Use AgentCore Runtime for sessions, memory and tool registration.
🔹 Implement Bedrock Guardrails and the ApplyGuardrail API to improve safety and control.
🔹 Add OpenTelemetry (OTEL) instrumentation for agent tracing and observability.
🔹 Implement episodic and semantic memory patterns for intelligent agents.
🔹 Design agent evaluation harnesses and regression testing approaches to measure AI-agent quality.
🌟 Why This Role Is Good for Your Experience
🔥 This role can be particularly valuable for engineers who want to move beyond traditional ETL and enter production-grade Agentic AI engineering. You can potentially gain experience across AWS, data engineering, LLM-powered agents, multi-agent communication, AI observability, memory architectures and evaluation.
📈 The combination of data pipelines + AWS + Agentic AI is powerful because modern AI systems depend heavily on high-quality data, reliable tools, secure model interactions and measurable agent behavior.
🛠️ Skills Required
☁️ Cloud: AWS, Amazon Bedrock, AWS AI Services
🤖 Agent Frameworks: Strands, LangGraph, CrewAI
🔌 AI Integration: MCP servers, tool authoring, A2A protocol
🧠 Agent Architecture: Agent registry, sessions, cross-agent state, memory
🛡️ Responsible AI: Bedrock Guardrails, ApplyGuardrail API
📡 Observability: OpenTelemetry, distributed tracing, agent tracing
🧪 AI Quality: Evaluation harnesses, regression testing, test datasets
🔄 Data Engineering: ETL, data pipelines, data processing and data quality
🐍 Programming: Strong Python or another programming language suitable for AI/data engineering
🗄️ Data: SQL, data modelling, structured/unstructured data handling
💡 Engineering: Git, APIs, debugging, CI/CD and production deployment practices
🎯 Expected Accenture Interview Rounds
🔹 Round 1 – Online/Technical Assessment: Coding, logical reasoning and practical technical problem-solving may be evaluated.
🔹 Round 2 – Data Engineering + AWS Technical Round: Expect questions around ETL, pipelines, data quality, AWS services and cloud architecture.
🔹 Round 3 – Agentic AI / GenAI Technical Round: Be ready to explain LangGraph/CrewAI/Strands, MCP, tool calling, agent state, memory, Bedrock and guardrails.
🔹 Round 4 – Architecture/Scenario Discussion: You may be asked to design an agentic solution, multi-agent workflow, observability strategy or evaluation framework.
🔹 Round 5 – Managerial/HR Discussion: Project ownership, communication, client interaction, teamwork, problem-solving and career motivation can be discussed.
💰 Salary Range
💵 The ₹18–30 LPA range is a reasonable target estimate for a specialized profile combining 3+ years of experience with AWS and Agentic AI skills, but it is not an officially published CTC range for this job posting. Public salary data for Accenture Data Engineer roles is generally lower: AmbitionBox reports approximately ₹4.7–18.4 LPA across 0–7 years, while its Data Engineer 2 data shows approximately ₹9.4–13 LPA for 2–9 years.
💡 Because this JD specifically asks for advanced Agentic AI capabilities, candidates with strong production experience in Bedrock, MCP, agent evaluation and observability may have better negotiation leverage than candidates applying with conventional ETL experience alone.
📚 Preparation Tips
🔥 1. Build one complete Agentic AI project: Create an agent using LangGraph, CrewAI or Strands with tools, memory and structured outputs.
☁️ 2. Go deep into Amazon Bedrock: Understand model invocation, tool usage, guardrails and production architecture rather than only knowing the service name.
🔌 3. Practice MCP: Build a small MCP server exposing tools such as database queries, file search or API calls.
🤝 4. Understand multi-agent architecture: Learn A2A concepts, agent discovery, registry patterns and how state moves between agents.
🧠 5. Prepare memory architecture: Clearly explain the difference between episodic and semantic memory and where each should be used.
📡 6. Learn agent observability: Understand traces, spans, latency, failures and OpenTelemetry instrumentation.
🧪 7. Design an evaluation framework: Prepare metrics for tool accuracy, task completion, hallucination, latency and regression testing.
📄 Resume Tips for This Role
🎯 Do not make your resume look like a generic Data Engineer CV. Put AWS + Agentic AI + Data Engineering near the top.
📌 Replace vague statements such as “worked on AI projects” with measurable achievements: “Built a LangGraph agent with 6 tools,” “implemented Bedrock Guardrails,” “reduced pipeline failure rate,” or “created automated agent evaluation tests.”
🔗 Mention specific technologies only when you have practical experience: Bedrock, LangGraph, CrewAI, Strands, MCP, A2A, AgentCore, OTEL, SQL, ETL.
🚀 How to Apply
👉 Application Link: Click Here
🤝 Referral Tip
Want to improve your chances of getting noticed?
Connect with someone at Accenture and request a referral—it can significantly enhance your visibility in the hiring process.
👉 Search for Job No. ATCI-5692862-S2063517 on Accenture’s official careers portal and apply through the official listing. Avoid third-party recruiters asking for payment—Accenture explicitly warns that candidates are never required to pay for employment.
🔎 Final Takeaway: This is a strong opportunity for a 3+ year engineer who wants to combine Data Engineering with production Agentic AI. The standout candidates will not simply know AWS or LLMs—they will be able to explain how to build, secure, observe, evaluate and operate AI agents as reliable enterprise systems.
