6,680 Big Data Analytics jobs in India

Summary:

The Senior Software Development Engineer is a hands-on developer specializing in Java, BI, ETL, Data Warehouse and Big Data systems development with a particular focus producing API/BI services in building product platforms.

Responsibilities:

• Work with cross-functional agile teams that include Front-end Developers, Report/BI Developers and Product Owners to implement the next generation of enterprise application
• Identify and resolve performance bottlenecks proactively
• Work with the customer support group as needed to resolve performance issues in the field
• Explore automation opportunity and develop tools to automate some of the day to day operations tasks
• Provide performance metrics and maintain dashboards to reflect production systems health
• Conceptualize and implement proactive monitoring where possible to catch issues early
• Experiment with new tools to streamline the development, testing and prod deployment
• Help the team improve with the usage of BI Applications best practices.
• Collaborate with other BI teams to improve the BI ecosystem.
• Creatively solve problems when facing constraints, whether it is the number of developers, quality or quantity of data, compute power, storage capacity or just time.
• Maintain awareness of relevant technical and product trends through self-learning/study, training classes and job shadowing

Requirements:

• 7-10 years of experience in Business Intelligence & Data Warehouse related projects in product or service-based organization
• • Experience of working with Databases like Oracle, SQL Server and have strong SQL knowledge and data modeling.
• Experience of working on data pipeline tools like NiFi for real time data flow system like Kafka
• Experience of working on automation in data flow process in a Big Data environment
• Experience of working in Agile teams
• Strong analytical skills required for debugging production issues, providing root cause and implementing mitigation plan
• Strong communication skills - both verbal and written - and strong relationship, collaboration skills and organizational skills
• Ability to multi-task across multiple projects, interface with external / internal resources and provide technical leadership to junior team members
• Ability to be high-energy, detail-oriented, proactive and able to function under pressure in an independent environment along with a high degree of initiative and self-motivation to drive results
• Ability to quickly learn and implement new technologies, and perform POC to explore best solution for the problem statement
• Flexibility to work as a member of a matrix based diverse and geographically distributed project teams

Top Skills:

• Design, develop, and deploy data pipelines in Apache NiFi for ingesting streaming data from Axon.

The Data Engineer is accountable for developing high quality data products to support the Bank's regulatory requirements and data driven decision making. A Data Engineer will serve as an example to other team members, work closely with customers, and remove or escalate roadblocks. By applying their knowledge of data architecture standards, data warehousing, data structures, and business intelligence they will contribute to business outcomes on an agile team.

Responsibilities

• Developing and supporting scalable, extensible, and highly available data solutions
• Deliver on critical business priorities while ensuring alignment with the wider architectural vision
• Identify and help address potential risks in the data supply chain
• Follow and contribute to technical standards
• Design and develop analytical data models

Required Qualifications & Work Experience

• First Class Degree in Engineering/Technology (4-year graduate course)
• 4+ years' experience implementing data-intensive solutions using agile methodologies
• Experience of relational databases and using SQL for data querying, transformation and manipulation
• Experience of modelling data for analytical consumers
• Ability to automate and streamline the build, test and deployment of data pipelines
• Experience in cloud native technologies and patterns
• A passion for learning new technologies, and a desire for personal growth, through self-study, formal classes, or on-the-job training
• Excellent communication and problem-solving skills

T
echnical Skills (Must Have)

• ETL: Hands on experience of building data pipelines. Proficiency in at least one of the data integration platforms such as Ab Initio, Apache Spark, Talend and Informatica
• Big Data: Exposure to 'big data' platforms such as Hadoop, Hive or Snowflake for data storage and processing
• Data Warehousing & Database Management: Understanding of Data Warehousing concepts, Relational (Oracle, MSSQL, MySQL) and NoSQL (MongoDB, DynamoDB) database design
• Data Modeling & Design: Good exposure to data modeling techniques; design, optimization and maintenance of data models and data structures
• Languages: Proficient in one or more programming languages commonly used in data engineering such as Python, Java or Scala
• DevOps: Exposure to concepts and enablers - CI/CD platforms, version control, automated quality control management

Technical Skills (Valuable)

• Ab Initio: Experience developing Co>Op graphs; ability to tune for performance. Demonstrable knowledge across full suite of Ab Initio toolsets e.g., GDE, Express>IT, Data Profiler and Conduct>IT, Control>Center, Continuous>Flows
• Cloud: Good exposure to public cloud data platforms such as S3, Snowflake, Redshift, Databricks, BigQuery, etc. Demonstratable understanding of underlying architectures and trade-offs
• Data Quality & Controls: Exposure to data validation, cleansing, enrichment and data controls
• Containerization: Fair understanding of containerization platforms like Docker, Kubernetes
• File Formats: Exposure in working on Event/File/Table Formats such as Avro, Parquet, Protobuf, Iceberg, Delta
• Others: Basics of Job scheduler like Autosys. Basics of Entitlement management

Certification on any of the above topics would be an advantage.

Job Family Group:
Technology

Job Family:
Applications Development

Time Type:
Full time

Most Relevant Skills
Please see the requirements listed above.

Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.

Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.
If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review
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View Citi's EEO Policy Statement and the Know Your Rights poster.

Job Summary

The Senior Analyst, Value Enablement & Optimization, will lead the development of data-driven frameworks, tools, and strategies that enhance value delivery across Pricing, Pre-Sales Enablement, and Customer Success. This role combines strategic thinking with technical expertise, requiring proficiency in advanced analytics, business intelligence, and automation to support scalable solutions that align with Mastercards growth objectives. The ideal candidate is a technically skilled, innovative, and collaborative problem solver with a passion for delivering impactful insights that drive customer and revenue success.

Key Responsibilities

Strategic Support

Design and implement value enablement frameworks for Pricing, Pre-Sales Enablement, and Customer Success, aligning with Mastercard's growth strategies.

Collaborate with global and regional stakeholders to ensure scalable solutions tailored to regional needs.

Provide data-driven recommendations to optimize pricing, enhance pre-sales propositions, and ensure customer success.

Develop project structures/frameworks and build/review presentations.

Conduct data sanity and hygiene checks to ensure data integrity.

Convert business problems into analytical problems for strategic development.

Technical Leadership

Develop and deploy advanced analytics tools like ROI calculators and value dashboards.

Use Python, R, and SQL for data analysis, modeling, and tool development.

Create dynamic dashboards and visualizations using business intelligence platforms.

Integrate AI/ML models to enhance tool accuracy and efficiency.

Drive process efficiency and scalability through automation and advanced analytics.

Value Enablement Initiatives

Build frameworks to measure and track customer value realization.

Design tailored customer solutions and business cases with predictive models and real-time insights.

Develop self-service analytics tools for actionable insights.

Encourage participation in Sandbox challenges and keep regular brainstorming sessions for new ideas.

Build agile and flexible solutions by understanding core business and context.

Revenue Optimization

Identify and implement revenue optimization opportunities through strategic analysis.

Monitor performance metrics to align with revenue goals and identify improvement areas.

Develop tools to track realized ROI and provide diagnostics for customer outcomes.

Collaboration & Team Enablement

Work closely with cross-functional teams to ensure seamless initiative execution.

Foster a collaborative and innovative environment, encouraging knowledge sharing.

Plan and lead working sessions with the team.

Provide mentorship and feedback for personal and professional growth.

Support training and enablement for internal teams on analytics tools and methodologies.

Stakeholder Management

Create and implement roadmaps for strategic initiatives.

Manage regular connects and alignment with stakeholders, handling escalations effectively.

Plan and articulate standups, agendas, action items, and next steps.

Qualifications

Experience

5+ years of experience in analytics, data science, pricing strategy, customer success, or related roles, ideally in the payments, financial services, or technology sectors.

Proven track record of developing and scaling data-driven tools and frameworks with measurable outcomes.

Expertise in programming (Python, R, SQL) and experience building scalable analytics solutions.

Proficiency in business intelligence tools (e.g., Tableau, Power BI) for creating dashboards and data visualizations.

Experience integrating AI/ML models to drive predictive insights and automate workflows is a strong advantage.

Skills

Strong technical acumen with the ability to design and implement advanced analytics and visualization solutions.

Exceptional analytical and problem-solving skills, with a focus on deriving actionable insights from complex datasets.

Excellent communication and stakeholder management skills, with the ability to translate technical insights into business impact.

Deep understanding of pricing strategies, customer success enablement, and revenue optimization principles.

Education

Bachelors degree in Data Science, Computer Science, Business Analytics, Economics, Finance, or a related field. Advanced degrees or certifications in analytics, data science, or AI/ML are preferred.

Apply before

Position:   Big Data Analytics + DevOps Engineer
Experience Required:  2–3 years (Freshers with strong knowledge are welcome)
Location:  Punawale, Pune
Employment Type:  Full-Time, Permanent
Compensation:
Freshers:  Stipend of ₹8,000 – 10,000 per month during the training/probation period
Experienced (2–3 years):  ₹2 – 5 LPA (based on skills and experience)

Job Description:
We are seeking a skilled Big Data Analytics + DevOps Engineer professional with expertise
in Kafka, Hadoop, Spark, Java, Data Analytics, and DevOps. The candidate will be
responsible for designing, developing, and managing large-scale data processing systems and
ensuring smooth deployment and scalability of data applications.

Key Responsibilities:

1. Develop and maintain big data pipelines using Hadoop and Spark.
2. Handle real-time data streaming using Kafka.
3. Implement data analytics solutions to extract insights from large datasets.
4. Collaborate with DevOps teams for CI/CD, automation, and deployment of data
applications.
5. Write efficient, maintainable code in Java and integrate with big data frameworks.
6. Monitor, troubleshoot, and optimize performance of data processing pipelines.

Mandatory Skills:

1. Big Data Technologies: Hadoop, Spark
2. Streaming Platforms: Kafka
3. Programming: Java
4. Data Analytics: Strong understanding of analytics concepts and processing large
datasets
5. DevOps Practices: CI/CD, automation, monitoring, deployment pipelines

Qualifications:

1. Proven experience in Kafka, Hadoop, Spark, Java, DevOps, and data analytics
2. Strong problem-solving and analytical skills
3. Ability to work collaboratively in a fast-paced environment

Fields with (*) are compulsory.

• Lead the design, development, and implementation of complex machine learning models and statistical analyses.
• Oversee data exploration, feature engineering, and model validation processes.
• Mentor and guide junior data scientists and analysts, fostering a culture of innovation and continuous learning.
• Collaborate with stakeholders across product, engineering, and business units to identify opportunities for data-driven improvements.
• Develop and maintain robust data pipelines and ensure data quality and integrity.
• Communicate complex findings and recommendations clearly and concisely to both technical and non-technical audiences.
• Research and evaluate new data science techniques, tools, and technologies.
• Drive the adoption of best practices in data science and machine learning.
• Develop and present strategic insights derived from data analysis to senior management.
• Ensure the scalability and performance of deployed models in production environments.
• Manage project timelines, resources, and deliverables effectively.
• Build and maintain strong relationships with key business partners.
• Contribute to the company's intellectual property through publications or internal knowledge sharing.
• Optimize existing algorithms and data processing workflows for efficiency.
• Stay abreast of the latest research in AI, machine learning, and big data technologies.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
• 5+ years of hands-on experience in data science, with a proven track record of delivering impactful results.
• Expertise in Python or R, including libraries like TensorFlow, PyTorch, scikit-learn, Pandas, and NumPy.
• Proficiency in SQL and experience with big data technologies (e.g., Spark, Hadoop, Hive).
• Strong understanding of machine learning algorithms (regression, classification, clustering, deep learning) and statistical modeling.
• Experience with cloud platforms (AWS, Azure, GCP) and their data services.
• Excellent problem-solving, analytical, and critical thinking skills.
• Demonstrated leadership and team management capabilities.
• Strong communication and presentation skills, with the ability to convey technical concepts to diverse audiences.
• Experience with data visualization tools (e.g., Tableau, Matplotlib) is a plus.
• Familiarity with MLOps principles and practices.
• Ability to work independently and as part of a collaborative team.

• Develop and implement advanced statistical models and machine learning algorithms to analyze large datasets.
• Clean, transform, and prepare data for analysis, ensuring data integrity and accuracy.
• Identify trends, patterns, and insights within data to address business challenges and opportunities.
• Design and conduct A/B tests and other experiments to evaluate hypotheses.
• Create data visualizations and reports to communicate complex findings to technical and non-technical stakeholders.
• Collaborate with engineering teams to deploy models and integrate data solutions into production environments.
• Stay abreast of the latest advancements in data science, machine learning, and big data technologies.
• Contribute to the development of data governance policies and best practices.
• Mentor junior data analysts and scientists as needed.
• Proactively identify areas for improvement in data collection, processing, and analysis methodologies.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Proven experience (5+ years) as a Data Scientist or in a similar analytical role.
• Proficiency in programming languages such as Python or R, and relevant data science libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Strong experience with SQL and NoSQL databases.
• Expertise in big data technologies (e.g., Spark, Hadoop, Hive).
• Solid understanding of statistical modeling, machine learning algorithms, and data mining techniques.
• Experience with data visualization tools (e.g., Tableau, Power BI, Matplotlib).
• Excellent problem-solving and critical-thinking skills.
• Strong communication and collaboration skills, with the ability to explain complex technical concepts clearly.
• Demonstrated ability to work independently and manage time effectively in a remote setting.

• Design, develop, and implement statistical models and machine learning algorithms to analyze large datasets and identify trends.
• Collect, clean, and preprocess data from various sources, ensuring data integrity and accuracy.
• Develop and maintain data pipelines for efficient data processing and retrieval.
• Visualize data and model results to effectively communicate findings to technical and non-technical stakeholders.
• Collaborate with product managers, engineers, and other data scientists to define data-driven product strategies and features.
• Perform exploratory data analysis to uncover hidden patterns and opportunities.
• Build and optimize predictive models for various business applications, such as customer segmentation, churn prediction, and fraud detection.
• Evaluate model performance and iterate on approaches to improve accuracy and efficiency.
• Stay abreast of the latest advancements in data science, machine learning, and artificial intelligence.
• Contribute to the development of internal tools and frameworks to enhance the data science workflow.
• Mentor junior data scientists and share knowledge within the team.
• Present findings and recommendations to senior leadership.
• Ensure data privacy and security protocols are followed.
• Develop A/B testing frameworks and analyze experimental results.
• Work with cloud platforms (AWS, Azure, GCP) for data storage and computation.
• Document methodologies, code, and findings thoroughly.
• Contribute to a culture of data-driven decision-making across the organization.
• Assist in defining key performance indicators (KPIs) and tracking business impact.
• Proactively identify opportunities for data-driven improvements in business processes.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• 3+ years of professional experience in data science or a similar role.
• Proficiency in programming languages such as Python or R, including relevant libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
• Strong understanding of statistical modeling, machine learning techniques, and experimental design.
• Experience with big data technologies and distributed computing frameworks (e.g., Spark, Hadoop).
• Experience with SQL and NoSQL databases.
• Familiarity with cloud computing platforms (AWS, Azure, GCP).
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation skills, with the ability to explain complex technical concepts to a diverse audience.
• Proven ability to work independently and manage multiple projects simultaneously in a remote setting.
• Experience with data visualization tools (e.g., Tableau, Matplotlib, Seaborn).
• Familiarity with software development best practices, including version control (Git).
• Experience with natural language processing (NLP) or computer vision is a plus.
• Demonstrated ability to deliver impactful results through data analysis.
• A proactive and curious mindset.