#ACN I&P - GN- SONG - AI & Data - Platforms - Data Engineering - Consultant

Job description

Entity: GN Song Practice: GN Song - Data & AI Title: Song Process Excellence | Data Engineering Consultant - CL9 Job Location: Gurgaon/ Bangalore/ Mumbai/ Hyderabad/ Pune/ Kolkata/ Chennai About Song - Data & AI Accenture Song uses AI, proprietary customer data, and product platforms to help clients improve customer experience and drive measurable growth across marketing, sales, commerce, and service. From strategy through execution, Song Data & AI helps organizations build and operationalize advanced capabilities - covering customer data unification, predictive analytics, and Generative AI (including agentic use cases) - to enable smarter decisioning like personalization and “next best action,” faster content and experience delivery, and more effective commerce and customer engagement. What's In It For You? Join a worldwide network of data engineers, architects, and governance practitioners building trusted, scalable data foundations for AI and digital transformation. Access world-class training, mentorship, and certifications across cloud data platforms, lakehouse and warehouse engineering, DataOps, data governance, and modern data architecture. Work on high-visibility engagements across Marketing, Sales, Commerce, Customer Service, and Digital Products - building data pipelines and products that power analytics, ML, Generative AI, and agentic solutions. Contribute to Accenture's reusable engineering components, reference implementations, technical standards, and delivery playbooks. What You Will Do As a Data Engineering Consultant, you will own the hands-on design, build, testing, deployment, and support of assigned data engineering workstreams. You will translate requirements into detailed technical solutions, implement architecture and governance standards, and guide junior team members to deliver trusted data products for analytics and AI. Analyze business and data requirements and translate them into source-to-target mappings, detailed designs, user stories, and executable engineering tasks. Design and build scalable batch and streaming pipelines, ETL/ELT workflows, curated data layers, and reusable data products using Python, SQL, Spark/PySpark, and platform-native services. Integrate structured, semi-structured, and unstructured data from enterprise applications, APIs, files, databases, and event streams. Implement logical and physical data models, lakehouse or warehouse structures, semantic or serving layers, and data interfaces within the agreed solution architecture. Apply data governance controls - including data quality, metadata, lineage, cataloguing, ownership, access, privacy, retention, and auditability - as part of engineering delivery. Implement DataOps practices such as source control, automated testing, CI/CD, orchestration, release promotion, monitoring, and incident resolution. Optimize pipelines and workloads for reliability, performance, scalability, and cost; troubleshoot failures and resolve production issues. Prepare governed, reusable, and well-documented data for BI, advanced analytics, ML, Generative AI, and agentic solutions. Collaborate with data architects, governance teams, application owners, analysts, and AI teams to ensure end-to-end integration and fit-for-purpose data delivery. Own an assigned module or workstream, manage day-to-day technical activities, review deliverables, and coordinate Analysts or Senior Analysts. Engage client product owners and technical stakeholders through requirement workshops, design walkthroughs, sprint reviews, status reporting, and solution demonstrations. Contribute reusable code, templates, technical documentation, reference implementations, and practice knowledge assets. Domain Focus Candidates should bring hands-on data engineering and data product delivery experience in one or more of the following domains: Marketing - customer data unification, campaign and media data, audience activation, personalization, and next-best-action enablement Sales - customer, outlet, product, and route-to-market data; revenue intelligence, forecasting, and sales performance analytics Commerce - product, catalog, order, transaction, inventory, and digital commerce data platforms Service - customer interaction, contact center, case, knowledge, and operational data for service transformation Design & Digital Products - product telemetry, clickstream and event data, experimentation, digital analytics, and reusable data services Who We Are Looking For Mandatory Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, Mathematics, Engineering, or a related discipline. 4-8 years of progressive experience in data engineering, data platform delivery, data integration, and/or data management, including ownership of production workstreams. Strong hands-on expertise in Python, SQL, and Spark/PySpark, with the ability to write scalable, maintainable, and production-quality code. Proven experience building and operating ETL/ELT pipelines, data transformation workflows, and reusable data products in enterprise or production environments. Strong foundations in distributed data processing, batch architectures, data integration, orchestration, and API- or event-based integration patterns; streaming experience is valuable. Solid experience with at least one major cloud platform - Azure, AWS, or GCP - and one modern data platform such as Databricks or Snowflake. Hands-on experience with lakehouse or warehouse technologies and tools for data processing, storage, transformation, orchestration, and workflow scheduling. Working knowledge of conceptual, logical, and physical data models, dimensional modelling, data products, semantic or serving layers, and enterprise integration patterns. Experience implementing data quality, metadata, lineage, cataloguing, access controls, privacy, retention, and master or reference data practices. Experience with Git-based development, CI/CD, automated testing, observability, release management, service-level monitoring, and incident response for data workloads. Working knowledge of security and platform fundamentals, including IAM, encryption, secrets management, networking, and workload isolation. Demonstrated ability to troubleshoot pipeline failures, optimize Spark or SQL workloads, and support production data solutions. Ability to prepare reliable data foundations for analytics, ML, Generative AI, and agentic solutions across structured, semi-structured, and unstructured data. Ability to work from a high-level architecture and produce detailed technical designs, mappings, implementation plans, and technical documentation. Strong stakeholder engagement, problem-solving, and communication skills, with the ability to explain technical choices to client and cross-functional teams. Experience leading a technical module or small workstream, estimating tasks, tracking dependencies and risks, reviewing work, and mentoring junior team members. What We Are NOT Looking For Profiles limited to reporting, dashboard development, or BI analysis without substantive data engineering and production ownership. Legacy ETL support profiles without hands-on experience in modern cloud data platforms, scalable engineering, testing, and automation. Platform administrators, database administrators, or tool specialists without end-to-end pipeline and data product development experience. Architecture-only, delivery-management-only, or coordination-only profiles that are not able to design, code, review, and troubleshoot data solutions. Candidates without evidence of independently owning a technical module or resolving production-grade data engineering problems. Accenture is an equal opportunities employer and welcomes applications from all sections of society and does not discriminate on grounds of race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, or any ot