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

Overview

As a Data Engineering Consultant, you will own the hands-on design, build, testing, deployment, and support of assigned data engineering workstreams, delivering trusted data products for analytics and AI. The role covers data engineering and data product delivery across Marketing, Sales, Commerce, Service, and Design & Digital Products.

What you'll do

• 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.
• 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.

What you'll need

• 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.

Nice to have

• Master's degree, recognized cloud or data platform certifications, or relevant consulting experience.
• Experience with tools such as Delta Lake, dbt, Apache Airflow, Azure Data Factory, Microsoft Fabric, AWS Glue, Kafka, BigQuery, or equivalent.
• Experience with change data capture, event-driven architectures, and near-real-time or streaming data platforms.
• Exposure to enterprise data catalog, governance, data quality, or MDM tools such as Microsoft Purview, Collibra, Alation, Informatica, or equivalent.
• Familiarity with data mesh, data fabric, domain-oriented data products, data contracts, and federated governance patterns.
• Experience in Agile delivery, client-facing consulting, proofs of concept, or contribution to reusable engineering assets and communities of practice.

Details

• Job locations: Gurgaon, Bangalore, Mumbai, Hyderabad, Pune, Kolkata, and Chennai.