{"id":195225,"url":"https://alion.io/job/mastercard-lead-data-engineer-4","title":"Lead Data Engineer","company":{"id":252,"name":"Mastercard","domain":"mastercard.com","url":"https://alion.io/company/mastercard","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":87,"open_postings":189,"ghost_share":0.016,"stale_share":0.423,"repost_share":0.079,"time_to_fill_p50_days":27,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Dublin, Ireland"],"countries":["IE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":89000,"max_usd":195000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":328},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Java","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Hadoop","optional":true},{"name":"pySpark","optional":true}],"status":"live","first_seen_at":"2026-09-03T09:02:09Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-10-31T09:14:25Z","board_verified":true,"closed_at":null,"days_open":58,"trust":{"level":"ok","repost_count":2,"flags":["repost"],"days_open":57},"description":"Our Purpose\nMastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.\nTitle and Summary\nLead Data EngineerLead Data EngineerEnterprise Credit Risk (ECR)\nWho is Mastercard?\nAt Mastercard technology, we work to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships, and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.\nOur decency quotient (DQ) drives our culture and everything we do inside and outside our company. We cultivate a culture of inclusion that respects individual strengths, perspectives, and experiences. We believe our differences enable us to be a better team, driving innovation and delivering better business outcomes.\nOverview\nThe Enterprise Credit Risk (ECR) team is seeking a Lead Data Engineer to help build and scale the next generation of data platforms that power credit decisioning, portfolio risk management, regulatory reporting, analytics, and AI-driven insights across Mastercard's lending and risk ecosystems.\nThe ideal candidate combines deep hands-on engineering expertise with technical leadership, enabling teams to build reliable, scalable, governed, and high-quality data products. This individual will lead the design and implementation of modern data engineering solutions spanning cloud platforms, large-scale data processing, data governance, and operational excellence.\nThis role will partner closely with Product Management, Risk Analytics, Data Science, Architecture, and Business stakeholders to simplify access to trusted data and accelerate innovation across the ECR program.\nRole\nAs a Lead Data Engineer, you will:\nLead the design, development, and evolution of enterprise-grade data platforms and pipelines supporting credit risk products, decisioning capabilities, and analytics solutions.\nArchitect and implement scalable ETL/ELT frameworks utilizing Databricks, Spark, Delta Lake, and cloud-native technologies.\nEstablish data quality, lineage, governance, observability, and monitoring capabilities to ensure trusted and compliant data products.\nDrive the migration and modernization of legacy data assets into cloud-based architectures and Data Lakehouse platforms.\nPartner with Risk, Product, Architecture, and Engineering teams to translate business requirements into scalable technical solutions.\nDefine and promote engineering standards, coding practices, testing frameworks,Data Quality frameworks, deployment automation, and operational excellence across the data ecosystem.\nLead technical design reviews and influence architectural direction for data-intensive applications and services.\nOptimize large-scale data processing workloads for performance, reliability, scalability, and cost efficiency.\nEnable AI and advanced analytics initiatives through creation of high-quality, reusable, governed data products.\nMentor, coach, and raise the technical capability of engineers across the organization by fostering a culture of ownership, continuous learning, accountability, and engineering excellence.\nShape strategic roadmap planning, technology evaluation, and delivery priorities across the ECR portfolio, balancing business outcomes, engineering feasibility, risk, compliance, and long-term platform sustainability.\nSupport regulatory, compliance, security, and audit requirements through robust engineering controls and documentation.\nOwn complex problems with dependencies across multiple services and facilitate cross-functional collaboration to drive resolution.\nConduct technical interviews, assess engineering talent, and contribute to raising the overall performance bar of the organization.\nAll About You\nThe ideal candidate for this position should have:\nEssential Skills & Experience\nStrong expertise in designing and implementing large-scale data engineering solutions and distributed data processing systems.\nAdvanced proficiency with Databricks, Apache Spark, Delta Lake, SQL, and Python.\nExperience building and operating cloud-based data platforms on Azure, AWS, or GCP.\nExpertise developing enterprise-grade ETL/ELT pipelines, streaming architectures, and data integration frameworks.\nStrong understanding of data modeling techniques for analytical and operational workloads.\nExperience implementing data quality frameworks, lineage, metadata management, and governance practices.\nExperience with Data formats ( Parquet, Avro, ORC )\nWorking knowledge of CI/CD pipelines, infrastructure-as-code, automated testing, and DevOps practices.\nExperience with Workflow orchestration Tools like Airflow\nStrong understanding of security, privacy, and compliance requirements associated with sensitive financial and customer data.\nProven ability to lead technical initiatives across multiple teams and influence engineering direction without direct authority.\nExcellent communication skills with the ability to collaborate effectively across technical and business functions.\nDemonstrated leadership in aligning engineering teams around shared goals, driving delivery through ambiguity, and creating clarity for stakeholders across product, risk, analytics, architecture, and operations.\nAbility to influence senior technical and business stakeholders, make thoughtful trade-off decisions, and guide teams toward pragmatic solutions that improve credit risk outcomes and operational resilience.\nKnowledge of Java Based application development is a huge Plus.\nLeadership Skills\nLead by influence across engineering, product, risk, analytics, and architecture teams to align priorities and deliver measurable business outcomes.\nCreate clarity in complex, ambiguous environments by translating business needs into actionable technical direction and execution plans.\nDevelop engineering talent through mentoring, knowledge sharing, design guidance, and constructive feedback.\nPromote a high-accountability culture focused on quality, reliability, security, compliance, and continuous improvement.\nCommunicate effectively with senior stakeholders and clearly articulate trade-offs, risks, dependencies, and delivery progress.\nPreferred Qualifications\nBachelor's degree in Computer Science, Engineering, Information Systems, or a related STEM discipline or alternative minimum of 10 years of experience in a related field.\nTechnical Skills\nPreferred expertise in:\nDatabricks\nApache Spark / PySpark\nHadoop\nDelta Lake\nSQL\nPython\nCorporate Security Responsibility\nAll activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:\nAbide by Mastercard’s security policies and practices;\n\nEnsure the confidentiality and integrity of the information being accessed;\n\nReport any suspected information security violation or breach, and\n\nComplete all periodic mandatory security trainings in accordance with Mastercard’s 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