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Mastercard
Lead, Big Data Analytics & Engineering
Mastercard
Pune•On-site•8-11 Yrs•1 hour ago
Job description
Overview
Mastercard powers economies and empowers people in 200+ countries and territories worldwide.
Responsibilities
- Lead the ingestion, transformation, aggregation, and processing of large scale datasets to enable advanced analytics and downstream consumption.
- Design, build, and maintain robust, scalable data pipelines across Hadoop and enterprise data platforms, ensuring high standards of data quality, reliability, performance, and availability.
- Drive data unification initiatives, integrating multiple structured and semi structured data sources into a cohesive, governed analytical foundation.
- Manipulate and analyse high volume, high velocity, and high dimensional datasets using modern big data frameworks.
- Analyse large volumes of transactional and product data to produce insights and actionable recommendations that support business growth and value realisation.
- Apply metrics, measurement frameworks, and benchmarking techniques to evaluate solution effectiveness and drive continuous improvement.
- Partner with Product Managers, Data Science, Platform Strategy, and Technology teams to understand analytical and data requirements and translate them into scalable engineering solutions.
- Act as a technical bridge between business, analytical, and engineering teams, clearly articulating architecture decisions, trade offs, and implementation approaches.
- Enable alignment across stakeholders to ensure data solutions are directly tied to business and customer outcomes.
- Identify innovation opportunities and deliver proofs of concept, prototypes, and pilot solutions aligned to near term and future business needs.
- Integrate new and emerging data assets that enhance existing platforms, products, and services, strengthening overall value propositions.
- Gather and synthesise feedback from clients, product, engineering, and sales teams to inform new solutions and product enhancements.
- Provide technical leadership, guidance, and mentorship to data engineers and analysts, setting standards for engineering quality, scalability, performance, and maintainability.
- Promote best practices in data modelling, pipeline design, performance optimisation, and data governance.
- Influence engineering standards, architectural consistency, and long term platform sustainability.
- Strong proficiency in Python, including Pandas, NumPy, PySpark, with hands on experience using Impala.
- Proven experience working on Hadoop based platforms, performing large scale data extraction, transformation, and processing.
- Strong SQL skills and experience working with both relational and distributed data stores.
Qualifications
- Hands on experience with ETL / ELT and data integration tools, such as Apache Airflow, Apache NiFi, Azure Data Factory, Pentaho, or Talend.
- Exposure to machine learning concepts and analytical techniques used in advanced data solutions.
- 8+ years of experience in data engineering, big data analytics, or enterprise data platforms, including 2+ years in a lead or technical leadership role.
- GenAI / LLM Skills (Preferred)
- Exposure to unstructured and semi-structured data processing (documents/logs/text) and building curated datasets for downstream consumption.
- Strong understanding of data governance, privacy, and security requirements when using enterprise data with AI (PII handling, access control, auditability).
- Familiarity with operationalizing AI data workflows (monitoring, data quality checks, reproducibility, and cost-aware scaling in cloud environments).
- Strong experience collecting, standardising, and summarising diverse datasets while identifying patterns, inconsistencies, and data quality issues.
- Solid understanding of how analytics, metrics, and visualisation support business decision making.
- Ability to comprehend complex operational systems and deliver scalable analytics and information products to a global user base.
- Comfortable operating in a fast paced, delivery driven environment, both as a hands on contributor and a technical leader.
- Ability to move seamlessly between business, analytical, and technical contexts, communicating clearly with diverse audiences.
- Demonstrates Mastercard’s DQ values, with a collaborative, inclusive, and customer centric mindset.
- Abide by Mastercard’s security policies and practices;
- Ensure the confidentiality and integrity of the information being accessed;
- Report any suspected information security violation or breach, and
- Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.