Website Accenture

Data Engineer – Accenture (India)

Opportunity Overview

Attribute Details
Organisation Accenture in India
Position Title Data Engineer (Project Role: Data Engineer)
Experience Requirement Minimum 2 years of dedicated experience in Databricks
Primary Location Gurugram, Haryana, India
Department / Practice AI Powered Tech Talent / Data Engineering Practice
Employment Type Full-Time, Permanent
Target Industry IT Services & Global Strategy Consulting
Academic Target Minimum 15 years full-time formal education (e.g., B.Sc, BCA, or 3-year Degree + High School)
Core Skill Stack Databricks, PySpark / Scala, ETL Pipelines, Data Modelling, Cloud Storage

Databricks ETL & Cloud Analytics Architecture

The Data Engineer at Accenture operates as a Subject Matter Expert (SME), designing, optimising, and deploying enterprise-grade data pipelines using the Databricks Lakehouse Platform:

┌───────────────────────────────────────────┐
│ 1. Data Ingestion & Integration           │ ➔ Extract raw structured/unstructured feeds using Databricks & Cloud Services
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 2. Pipeline Construction & ETL Scripting  │ ➔ Develop transformation workflows in Python (PySpark) or Scala on Databricks
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 3. Data Modeling & Quality Assurance      │ ➔ Build scalable dimensional schemas and enforce data validation protocols
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 4. Performance Monitoring & Optimization  │ ➔ Optimize cluster compute, tune Delta Lake tables, and maintain production SLAs
└───────────────────────────────────────────┘

Key Responsibilities

  • Databricks Pipeline Engineering: Design, build, and maintain production ETL/ELT pipelines leveraging Databricks Unified Data Analytics Platform.

  • Independent Execution & SME Ownership: Work independently to solve complex data engineering issues, serving as a Subject Matter Expert within client delivery teams.

  • Cross-Functional Collaboration: Partner with data architects, analysts, and business stakeholders to translate raw data requirements into scalable storage schemas.

  • Pipeline Monitoring & Optimization: Continuously monitor production data workflows, tuning cluster configurations and query performance for speed and cost efficiency.

  • Data Migration & Integration: Execute secure data migration strategies to deploy data assets across disparate legacy and cloud systems.

Qualification Matrix & Technical Skill Stack

Core Requirements

Category Specifications
Educational Background Minimum 15 years full-time education (Standard 10+2+3 or equivalent graduation pattern).
Professional Experience 2+ years of direct hands-on experience using Databricks Unified Data Analytics Platform.
Primary Skill (Must Have) Expert-level proficiency in Databricks (Delta Lake, Auto Loader, Spark SQL, Databricks Workflows).
Programming Languages Python (PySpark) or Scala for data manipulation and distributed computing scripts.
Data Modeling & Cloud Strong grounding in dimensional data modeling (Star/Snowflake schema) and cloud object storage (AWS S3, Azure ADLS, or GCP).
Consulting Competencies Strong problem-solving skills, willingness to actively contribute in team architectural discussions, and stakeholder communication.

Key Focus Areas for Interview Preparation

  1. Databricks & Spark Optimization: Practice PySpark syntax, Spark performance tuning (partitioning, caching, broadcast joins), and Delta Lake features (Z-Ordering, Time Travel, Liquid Clustering).

  2. ETL Design Scenarios: Prepare to walk through end-to-end data pipeline architectures handling batch and real-time streaming data on Databricks.

  3. Data Quality & Schema Design: Review star-schema design principles, slow-changing dimensions (SCD Type 1/2), and data validation frameworks within Databricks pipelines.

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