Associate Data Engineering Manager

Design, build, and maintain scalable data pipelines and data platforms using modern programming languages, cloud data warehouses, and distributed data processing frameworksDevelop resilient data architectures across data warehouses, data lakes, Lakehouse platforms, and streaming environments to support enterprise reporting, business intelligence, advanced analytics, and machine learning use casesLead end-to-end delivery of analytics data products, from requirements understanding and solution design through development, deployment, production support, monitoring, and performance optimizationServe as a Scrum Master / Agile delivery lead for data engineering workstreams by facilitating sprint planning, daily stand-ups, backlog refinement, sprint reviews, retrospectives, and release planningPerform PMO and delivery governance activities including project tracking, dependency management, RAID management, sprint progress reporting, stakeholder updates, and timely escalation of risks, issues, and blockersCollaborate with business stakeholders, product owners, data engineering teams, analytics teams, and data science partners to translate business needs into scalable, secure, and reusable data solutionsEnsure data security, governance, quality, lineage, standardization, and compliance are embedded across data pipelines, platforms, and analytics data productsBuild, optimize, and support ETL/ELT pipelines, data models, orchestration workflows, and reusable data assets that improve reliability, performance, and maintainabilityDrive continuous improvement in engineering practices through code reviews, technical documentation, automation, CI/CD adoption, monitoring standards, and platform optimizationMentor and guide junior data engineers by sharing best practices, supporting technical problem-solving, and promoting a culture of ownership, collaboration, and delivery excellenceCreate and deliver clear technical updates, delivery status summaries, and stakeholder communications for both technical and non-technical audiences, ensuring transparency and alignment across teamsComply 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 regard 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 Builder

Design, develop, and deploy AI-powered solutions using no-code, low-code, and advanced platforms, translating business needs into scalable applications that enhance products, workflows, and decision-makingRequired Qualifications: Bachelor's or master's degree in computer science, Engineering, Information Systems, Data Science, or a related technical field7+ years of relevant experience in Data Engineering, Analytics Engineering, Data Platform Development, or enterprise data solution deliverySolid hands-on expertise in Python and/or Scala/Java for building scalable data pipelines, automation frameworks, and production-grade data solutionsExperience designing, building, and optimizing ETL/ELT pipelines across batch and streaming data environmentsSolid experience with modern big data and distributed processing technologies such as Apache Spark, Databricks, Snowflake, AWS Redshift, and/or KafkaExtensive experience working with Azure cloud services and cloud-native data platform capabilitiesSolid understanding of data architecture concepts, including data modeling, dimensional modeling, warehousing, Lakehouse architecture, metadata management, and data governanceExperience with orchestration, DevOps, and engineering tools such as Airflow, CI/CD pipelines, Git, Linux, and Shell ScriptingHands-on experience working in Agile/Scrum delivery models, including sprint execution, backlog coordination, dependency tracking, risk/issue management, and stakeholder reportingAdvanced SQL skills, including complex query development, data manipulation, performance tuning, optimization, and large-scale data validationSolid communication .