AWS Data Engineer

About Us Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery.WHY JOIN CAPCO? You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.MAKE AN IMPACT Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.#BEYOURSELFATWORK Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.CAREER ADVANCEMENT With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.DIVERSITY & INCLUSION We believe that diversity of people and perspective gives us a competitive advantage.Location - BangaloreSkills and Qualifications:Job Description Data EngineerWe are looking for a Data Engineer with strong experience in building and operationalizing data pipelines, ETL workflows, and analytics platforms using PySpark, Apache Airflow, and AWS data services.Key ResponsibilitiesBuild scalable ETL/ELT pipelines using PySpark on distributed processing frameworksOrchestrate workflows using Apache Airflow (DAG design, scheduling, monitoring)Develop data ingestion and transformation jobs using AWS GlueManage secure, compliant data access using AWS Lake FormationMaintain and optimize AWS Glue Data Catalog for metadata, schema, and table managementWork with analytics teams to publish datasets for BI and dashboardsBuild and support visualizations using Amazon QuickSightEnsure data quality, performance, and reliability across all pipelinesRequired SkillsStrong hands-on experience with PySpark for large-scale data processingDeep knowledge of Airflow DAGs, operators, sensors, and CI/CD integrationExpertise in AWS Glue (ETL jobs, crawlers, Glue Studio, Glue Job Bookmarks)Experience with Lake Formation permissions, governance, and data lakesFamiliarity with Glue Data Catalog for metadata managementAbility to build dashboards in Amazon QuickSightUnderstanding of data modeling, partitioning, and performance optimizationNice to HaveExperience with S3, Athena, Redshift, or EMRKnowledge of Python-based automation and testingExposure to cloud-native DevOps (IaC, Terraform/CloudFormation)

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