Data Engineer

Job Description: Min 3 and max upto 5. Must Have 'Cloud & Data Engineering (AWS) Strong hands‑on experience with AWS data services, including: - Amazon S3 - AWS Glue - Amazon Athena - Amazon Redshift - Amazon EMR Experience designing cloud‑native data lakes and data warehouse architectures Solid understanding of batch data pipelines and basic exposure to streaming concepts SQL & Python (Mandatory) Strong SQL skills (mandatory) Writing complex queries, joins, aggregations, and transformations Experience working with large datasets in Redshift / Athena Strong Python skills (mandatory) Python for data engineering and ETL use cases Experience with Py Spark / Spark is a strong plus Good understanding of data modeling, transformations, and performance tuning Data Processing & Engineering Hands‑on experience with distributed data processing frameworks (Spark / Py Spark) Experience handling structured and semi‑structured data Understanding of schema evolution, data quality checks, and validation logic Dev Ops & Platform Basics Working knowledge of Infrastructure as Code (Terraform and/or Cloud Formation) Basic experience with CI/CD pipelines for data workloads Understanding of logging and monitoring using Cloud Watch Collaboration Ability to work closely with architects, Dev Ops, QA, and business stakeholders Good communication skills to explain technical concepts clearly Good to Have Exposure to streaming technologies such as Amazon Kinesis or Kafka Familiarity with Lakehouse and modern data platform patterns Experience integrating AWS data platforms with BI / reporting tools Basic knowledge of data governance, data quality, and metadata concepts Awareness of AWS cost optimization best practices Experience working in Agile delivery models, with global clients Exposure to AI / ML Key Responsibilities Data Engineering & Development Design and build scalable ETL / ELT pipelines on AWS Develop SQL‑based data transformations and Python‑based data pipelines Implement data ingestion pipelines using AWS services such as S3, Glue, EMR Build data models optimized for analytics, performance, and cost efficiency Platform & Operations Support deployment and execution of data pipelines across environments Monitor pipeline performance, reliability, and data quality Troubleshoot data pipeline issues and perform root‑cause analysis Apply best practices for security, reliability, and scalability Collaboration & Delivery Work closely with architects and product teams to understand requirements Translate business and analytics needs into working AWS data solutions Contribute to documentation, code reviews, and engineering standards Location: DGS India - Pune - Indiqube Orchid Brand: Merkle Time Type: Full time Contract Type: Permanent Can’t find a suitable job? Sign up for job alerts tailored to your interests and be first in line for new opportunities. About dentsu For over 120 years, innovation has been a core tenet of our offering – exploring new ways to reach, engage and nurture relationships with audiences. Together we drive a multiplier effect for clients at a global scale, through the development of Integrated Growth Solutions that are underpinned by our promise to clients: innovating to impact. Be a force for good. Sustainability is a vital part of our business and an important area of focus for our clients. We’re leading the way – helping to build a more sustainable planet. Dream loud. In this moment of transformation, we need our people to be fearless, embracing change and ambiguity, driven by the love for their work and excitement for the future. Team without limits. We create opportunities for connection and collaboration between our colleagues and clients, building a sense of belonging and having some fun along the way. Find out more about us Who we are Our Social Impact Our work