You will be taken to Deloitte’s own careers page to apply. Job ID 60569644.
Job description
Role: Data Engineer Experience: 3-6 years The Data Engineer is responsible for designing, developing, and maintaining scalable data pipelines and data platforms that enable analytics, reporting, and AI/ML solutions. This role works independently on moderately complex data engineering challenges, partners with business and technology stakeholders to understand requirements, and develops reliable data solutions that support enterprise data needs. The Data Engineer serves as a technical resource to team members, provides guidance on best practices, and contributes to the continuous improvement of data platforms and engineering processes.
Primary Responsibilities
Design, develop, and maintain scalable data ingestion, transformation, and integration pipelines
Build and optimize ETL/ELT workflows to process structured and unstructured data from multiple sources
Explore opportunities
Find Your Next MNC Job
Stop checking thousands of career pages. Find MNC jobs in one place.
Find. Explore. Apply. Discover jobs from leading companies in one place and apply directly on their official career websites.
01
Discover MNC Jobs
Browse fresh opportunities from 1000+ top companies in one place.
02
Open the Opportunity
Select the job that matches your skills, location and experience.
03
Apply on Official Website
Apply directly through the company’s official career page.
Analyze business and customer requirements and translate them into technical data solutions
Identify and resolve non-standard data challenges and moderately complex engineering problems
Develop and maintain data models, data quality frameworks, and validation processes
Collaborate with data analysts, data scientists, product teams, and business stakeholders to deliver trusted data assets
Monitor and optimize pipeline performance, reliability, scalability, and cost efficiency
Support cloud-based data platforms and data warehouse/lakehouse environments
Implement data governance, security, lineage, and compliance standards across data assets
Create technical documentation, design specifications, and operational procedures
Participate in code reviews and promote engineering best practices for development, testing, and deployment
Troubleshoot production issues, perform root-cause analysis, and implement sustainable solutions
Contribute to automation, CI/CD processes, and continuous improvement initiatives across the data ecosystem
Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so
Required Qualifications:
Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related field
3+ years of experience in data engineering, software engineering, or big data development
Experience developing ETL/ELT pipelines and working with large-scale datasets
Experience with data warehouses, data lakes, and modern data architectures
Solid proficiency in SQL and Python for data processing and engineering
Knowledge of cloud-based data platforms such as Azure, AWS, or Google Cloud
Understanding of data modeling, data quality, and data governance principles
Proven solid analytical and problem-solving skills
Proven excellent communication and stakeholder collaboration skills
Proven ability to work independently with minimal supervision and handle multiple priorities
Preferred Qualifications
Cloud platform certifications in Azure, AWS, or GCP
Experience with Databricks, Spark/PySpark, Snowflake, BigQuery, or similar cloud data technologies
Experience supporting analytics, business intelligence, or AI/ML workloads
Experience in healthcare, financial services, or other highly regulated industries
Familiarity with workflow orchestration tools such as Airflow, Azure Data Factory, or Cloud Composer
