Mastercard

2 days ago

Senior Analyst

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 minutes agoStatus: Live

Job Summary

Design, build, and operate scalable data pipelines and curated datasets that power analytics products, reporting, and advanced modeling. Partner with Product, Data Science, and Platform teams to ensure reliability, performance, data quality, and governance across batch and streaming workloads. Build ETL/ELT jobs with PySpark/Spark and SQL, automate data quality and lineage, optimize cost and performance, and support CI/CD, DevOps practices, and on-call operations.

Location: Pune, Gurugram

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Mid level

Key Responsibilities

•Build and maintain robust ETL/ELT pipelines for ingestion, transformation, and aggregation of large-scale datasets on Hadoop and enterprise platforms.
•Develop high-performance distributed data processing jobs using PySpark/Spark, Python, and SQL (including Impala where applicable).
•Translate requirements into reusable, governed data models (facts/dimensions, curated layers, and semantic-ready datasets).
•Implement automated data quality checks, reconciliation, lineage documentation, and monitoring to ensure trust in downstream analytics and AI use cases.
•Optimize pipeline performance and cost through partitioning, file formats, compute tuning, and efficient query patterns; support CI/CD for data workflows and on-call operations.

Key Requirements

•5+ years of relevant experience in data engineering or big data analytics engineering.
•Hands-on experience building production-grade data pipelines on big data platforms, including the Hadoop ecosystem (e.g., HDFS, Hive, Impala, YARN, Oozie).
•Proficiency in PySpark and Python with strong SQL skills across distributed and relational data stores.
•Experience with orchestration/integration tools such as Apache Airflow, Apache NiFi, Azure Data Factory, Pentaho, or Talend.
•Bachelor’s degree in computer science, engineering, or equivalent practical experience.

Experience:5+ yearsData engineeringBig data analytics engineeringHadoopCloud data platformsGenAI/LLM

Education:Bachelor's in computer science, Engineering, or equivalent practical experience

Skills:Problem-solvingCommunicationRoot-cause analysisDebuggingStakeholder collaboration

Tech Stack:HadoopHDFSHiveImpalaYARNOoziePySparkSparkPythonSQLApache AirflowApache NiFiAzure Data FactoryPentahoTalendCDCSCD Type 1SCD Type 2ParquetORC

Company Brief

Mastercard

Mastercard is a global payments and technology company that processes transactions, provides fraud prevention and data services, and builds payment infrastructure for consumers, businesses, merchants and governments across 200+ countries.

Industry: Payments

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Purchase, New York, United States

Founded: 1966

Glassdoor: 4.1

Website LinkedIn Glassdoor

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Mastercard

2 days ago

Senior Analyst

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 minutes agoStatus: Live

Job Summary

Location: Pune, Gurugram

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Mid level

Key Responsibilities

•Build and maintain robust ETL/ELT pipelines for ingestion, transformation, and aggregation of large-scale datasets on Hadoop and enterprise platforms.
•Develop high-performance distributed data processing jobs using PySpark/Spark, Python, and SQL (including Impala where applicable).
•Translate requirements into reusable, governed data models (facts/dimensions, curated layers, and semantic-ready datasets).
•Implement automated data quality checks, reconciliation, lineage documentation, and monitoring to ensure trust in downstream analytics and AI use cases.
•Optimize pipeline performance and cost through partitioning, file formats, compute tuning, and efficient query patterns; support CI/CD for data workflows and on-call operations.

Key Requirements

•5+ years of relevant experience in data engineering or big data analytics engineering.
•Hands-on experience building production-grade data pipelines on big data platforms, including the Hadoop ecosystem (e.g., HDFS, Hive, Impala, YARN, Oozie).
•Proficiency in PySpark and Python with strong SQL skills across distributed and relational data stores.
•Experience with orchestration/integration tools such as Apache Airflow, Apache NiFi, Azure Data Factory, Pentaho, or Talend.
•Bachelor’s degree in computer science, engineering, or equivalent practical experience.

Experience:5+ yearsData engineeringBig data analytics engineeringHadoopCloud data platformsGenAI/LLM

Education:Bachelor's in computer science, Engineering, or equivalent practical experience

Skills:Problem-solvingCommunicationRoot-cause analysisDebuggingStakeholder collaboration

Tech Stack:HadoopHDFSHiveImpalaYARNOoziePySparkSparkPythonSQLApache AirflowApache NiFiAzure Data FactoryPentahoTalendCDCSCD Type 1SCD Type 2ParquetORC

Company Brief

Mastercard

Industry: Payments

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Purchase, New York, United States

Founded: 1966

Glassdoor: 4.1

Website LinkedIn Glassdoor