1 month ago
Bengaluru, IndiaMid Level / Senior
Responsibilities
- Analyze business and data requirements and translate them into source-to-target mappings, detailed designs, user stories, implementation plans, and engineering tasks.
- Design and build scalable batch and streaming pipelines, ETL/ELT workflows, curated data layers, and reusable data products.
- Integrate structured, semi-structured, and unstructured data from enterprise applications, APIs, files, databases, and event streams.
- Implement data models, lakehouse or warehouse structures, semantic or serving layers, and data interfaces.
- Apply data quality, metadata, lineage, cataloguing, ownership, access, privacy, retention, and auditability controls.
- Implement DataOps practices including source control, automated testing, CI/CD, orchestration, release promotion, monitoring, and incident resolution.
- Optimize pipelines and workloads for reliability, performance, scalability, and cost, and resolve production issues.
- Prepare governed data foundations for BI, advanced analytics, ML, Generative AI, and agentic solutions.
- Collaborate with architects, governance teams, application owners, analysts, AI teams, product owners, and technical stakeholders.
- Own a technical module or workstream, coordinate Analysts or Senior Analysts, review deliverables, estimate tasks, track dependencies and risks, and mentor junior team members.
- Conduct requirement workshops, design walkthroughs, sprint reviews, status reporting, and solution demonstrations.
- Contribute reusable code, templates, technical documentation, reference implementations, and practice knowledge assets.
Requirements
- 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 production workstream ownership.
- Strong hands-on expertise in Python, SQL, and Spark/PySpark, with the ability to write scalable, maintainable, production-quality code.
- Experience building and operating ETL/ELT pipelines, data transformation workflows, and reusable data products in enterprise or production environments.
- Foundations in distributed data processing, batch architectures, data integration, orchestration, and API- or event-based integration patterns; streaming experience is valuable.
- Experience with at least one major cloud platform—Azure, AWS, or GCP—and one modern data platform such as Databricks or Snowflake.
- Experience with lakehouse or warehouse technologies and tools for data processing, storage, transformation, orchestration, and workflow scheduling.
- Knowledge of conceptual, logical, and physical data models, dimensional modeling, 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 IAM, encryption, secrets management, networking, and workload isolation.
- Ability to troubleshoot pipeline failures, optimize Spark or SQL workloads, and support production data solutions.
- Ability to produce detailed technical designs, mappings, implementation plans, and technical documentation from high-level architecture.
- Strong stakeholder engagement, problem-solving, and communication skills, including 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.
- Preferred qualifications include a Master’s degree, recognized cloud or data platform certifications, consulting experience, and experience with modern data, governance, streaming, data mesh, or client delivery practices.
Benefits
- Worldwide network of data engineers, architects, and governance practitioners working on trusted data foundations for AI and digital transformation.
- World-class training, mentorship, and certifications across cloud data platforms, lakehouse and warehouse engineering, DataOps, data governance, and modern data architecture.
- High-visibility client engagements across marketing, sales, commerce, customer service, and digital products.
- Opportunities to contribute reusable engineering components, reference implementations, technical standards, delivery playbooks, and communities of practice.
- Job locations include Gurgaon, Bangalore, Mumbai, Hyderabad, Pune, Kolkata, and Chennai.
Tech Stack
Apache AirflowApache KafkaApache SparkAWSAzureDatabricksdbtGitGoogle BigQueryGoogle Cloud PlatformInformaticaPythonSnowflakeSQL
Categories
Data Engineering
About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.