Data Analyst

Software Engineer AI

AVP, Digital Analytics

Senior Data Scientist III - Agentic AI

SAP Basis Engineer

Manager, Data Engineering

Sr. Software Engineer - Ag & Trading .Net

Senior Data Scientist - Machine Learning

AVP – Model Ops Engineer

Software Engineer

Responsibilities: Database Maintenance &amp; Support <ul><li>Assist in the maintenance of relational databases (Oracle, SQL Server).</li><li>Support schema creation, updates, normalization, and performance tuning.</li><li>Monitor data quality, identify inconsistencies, and help ensure data integrity across systems.</li></ul> Data Migration &amp; Platform Transition <ul><li>Support data extraction, transformation, and loading (ETL) during system migrations and platform upgrades.</li><li>Assist in transitioning data between legacy environments and modern systems, including Databricks.</li><li>Validate migrated data and document reconciliation results. Data Analysis &amp; Querying</li><li>Write and execute SQL queries for analysis, verification, and operational needs in Oracle and SQL Server environments.</li><li>Participate in exploratory and diagnostic analysis to uncover trends and support business decisions.</li></ul> Documentation &amp; Governance <ul><li>Maintain accurate documentation of data processes, transformations, and system behaviors.</li><li>Support compliance with organizational standards for data governance and metadata management.</li></ul> Requirements: Required Skills &amp; Qualifications <ul><li>Bachelor’s degree in Computer Science, Data Analytics, Information Systems, Engineering, or related field.</li><li>Foundational understanding of relational database concepts and SQL.</li><li>Introductory experience or coursework using Oracle, SQL Server, or similar RDBMS platforms.</li><li>Strong analytical thinking, attention to detail, and problem solving skills.</li><li>Ability to collaborate with technical teams in a fast paced environment.</li></ul> Preferred Skills &amp; Qualifications <ul><li>Exposure to Databricks or other big data / cloud analytics technologies.</li><li>Understanding of ETL concepts, data pipelines, or data transformation frameworks.</li><li>Familiarity with PL/SQL, SSIS, or other database programming tools.</li><li>Interest in learning modern data lake, delta, or virtualization technologies (e.g., Denodo).</li></ul>

Responsibilities: Design &amp; Build/Development &amp; Support <ul><li>Develop multi-agent workflow automation patterns using Agentic AI</li><li>Process redesign and mapping to agentic workflow patterns</li><li>Architect scalable micro-services that wrap LLM/RAG/Agent workflows (Python).</li><li>Implement robust prompt-engineering patterns, retrieval pipelines, and caching for AI Assistants and AI Agents</li><li>Profile inference latency, GPU/CPU utilization, and memory</li><li>Writes and maintains highly complex unit tests and integration tests, and performs debugging to maintain the quality and performance of the software/AI solutions</li><li>Lead bug-fix, security-patch, and performance-tuning sprints for live AI Assistants and AI Agents</li><li>Leads the establishment and maintenance of comprehensive documentation for agents, applications, deployment processes and system configurations.</li></ul> Operational Excellence&nbsp;<ul><li>Own on-call runbooks, SLOs, and incident reviews; embed observability</li><li>Leads and mentors technical support and troubleshooting for highly complex issues with deployed applications to ensure minimal downtime and fast resolution.&nbsp;</li></ul> Collaboration &amp; Leadership <ul><li>Leads cross functional team of engineers to gather requirements and deliver solutions that meet business needs.</li><li>Experience working within product and agile delivery frameworks including backlog management and cross-functionality delivery and full E2E engineering.&nbsp;</li><li>Experience with enterprise architecture, data governance, or security standards in the context of digital solution delivery as part of E2E engineering and solution architecture.&nbsp;</li></ul> Capability &amp; Technology Stack Exposure <ul><li>Experience with digital technologies in the commercial &amp; sales management capability (e.g. Salesforce, Service Cloud, CPQ, Salesloft, ChatGPT or similar platforms)&nbsp;</li></ul> Requirements: <ul><li>Minimum requirement of 2 years of building production software or data platforms.&nbsp;</li><li>Typically reflects 3 years or more with 2+ years of cloud-native AI/ML or GenAI systems (Azure, AWS, or GCP) or 2+ years of software development.&nbsp;</li></ul>

Responsibilities: <ul><li>Lead and deliver Digital Measurement Analytics for Digital Platforms in Agile methodology to drive digital data strategy that includes collection &amp; validation of data and reporting.</li><li>Partner with onsite Stakeholders and various process owners in marketing, technology and digital teams to plan and implement strategic priorities.</li><li>Present analytical results and actionable recommendations to key stakeholders on a recurring cadence, and propose innovative analytics solutions to drive business growth and enhance the customer experience.</li><li>Responsible for team’s effective communication, project deliverables, coaching team on adhering to expected timelines, accuracy &amp; compliance with meticulous project management.</li><li>Lead internal initiatives, perform other duties and/or special projects, as assigned.</li></ul> Requirements: <ul><li>Degree in Statistics, Mathematics, Economics, Engineering, or any other quantitative streams with 4+ years of hands-on relevant Analytics experience.</li><li>Minimum 6+ years of relevant work experience in lieu of specified degree.</li><li>2+ years of experience in Digital or Web Analytics Domain utilizing tools such as Adobe or Google Analytics and 2+ years of experience in SAS or SQL.</li><li>1+ years of Experience in Managing People, Coordinating work, Supervising, Evaluating performance and ensuring employees are trained.</li><li>Minimum 1+ years of experience in Data visualizations tools such as Tableau to drive insights.</li><li>Excellent oral and written communication skills with ability to collaborate, articulate and present solutions cutting across functional/agile teams in a simple yet compelling manner</li></ul>

Responsibilities: <ul><li>Lead experimentation and model development for AI/ML solutions in legal products.</li><li>Design and evaluate NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies).</li><li>Define agentic workflows and reasoning strategies for multi-step legal tasks.</li><li>Define retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).</li><li>Analyze large-scale legal datasets to extract insights and improve model performance.</li><li>Establish best practices for model evaluation, validation, and benchmarking.</li><li>Translate experimental results into clear product recommendations and business impact.</li><li>Collaborate with product, legal experts, and engineers to align solutions with user needs.</li><li>Mentor team members and provide technical leadership in data science and AI.</li></ul> Requirements: <ul><li>Bachelor’s or Master’s degree in a relevant field (e.g., Computer Science, Data Science, Statistics, Mathematics).</li><li>Strong experience in machine learning, NLP, and LLM-based modeling.</li><li>Proven experience designing and running experiments, including model evaluation and iteration.</li><li>Experience with generative AI techniques (e.g., prompt engineering, RAG).</li><li>Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases.</li><li>Experience designing agentic workflows and reasoning strategies, with hands-on experience applying agent frameworks (e.g., LangChain, LangGraph, AutoGen) in real-world use cases.</li><li>Proficiency in Python and data analysis tools.</li><li>Ability to define retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).</li><li>Able to analyze large-scale legal datasets to extract insights and improve model performance.</li></ul>

Responsibilities: <ul><li>Designs, builds, and supports SAP platform solutions by applying sound engineering practices to ensure system stability, performance, and scalability.</li><li>Utilizes enterprise tools and methodologies to automate system deployments, transport, and administrative activities, enabling reliable and repeatable releases.</li><li>Collaborate with cross‑functional partners to translate moderately complex business and technical requirements into effective SAP Basis solutions.</li><li>Performs system validation, technical testing, monitoring, and troubleshooting to maintain the quality and availability of SAP systems.</li><li>Contributes to continuous improvement initiatives by identifying opportunities to enhance SAP Basis processes, automation, and operational efficiency.</li><li>Creates and maintains clear technical documentation for SAP landscapes, configurations, and operational procedures.</li><li>Provides technical support for moderately complex SAP Basis issues, ensuring timely resolution and minimal impact to business operations.</li><li>Drives automation and quality through the use of enterprise testing and deployment tools, including Tosca for test automation and qTest for test management, execution, and traceability.</li><li>Develops, executes, and maintains manual and automated test assets for SAP systems, ensuring end‑to‑end coverage across SIT, UAT, regression, and integration testing cycles.</li><li>Performs system validation, monitoring, defect analysis, and troubleshooting in partnership with development, security, and infrastructure teams to ensure reliability and performance.</li></ul> Requirements: <ul><li>Minimum requirement of 5 years of relevant work experience with SAP Basis.&nbsp;</li><li>Demonstrated expertise in SAP Basis engineering, with strong knowledge of SAP system architecture, technical landscape design, system administration, upgrades, transport, performance monitoring, and operational support across multiple SAP environments.</li><li>Experience managing and supporting SAP technical operations, including system availability, batch processing, interfaces, background jobs, monitoring, troubleshooting, and incident resolution in alignment with enterprise service expectations.</li><li>Proven ability to support SAP Fiori and UI technical components from a Basis perspective, including configuration, activation, performance support, and coordination with security and application teams.</li><li>Experience leveraging scripting and technical tools (such as SQL and automation utilities) to improve operational efficiency, support system analysis, and standardize SAP Basis activities.</li><li>Working knowledge of cloud‑hosted and hybrid SAP landscapes, including experience supporting SAP systems on platforms such as Microsoft Azure, AWS, or Google Cloud, with an understanding of infrastructure, connectivity, and platform operations.</li></ul>

Responsibilities: <ul><li>DATA &amp; ANALYTICAL SOLUTIONS: Oversees the development of data products and solutions using big data and cloud based technologies, ensuring they are designed and built to be scalable, sustainable and robust.</li><li>DATA PIPELINES: Develops and monitors streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.</li><li>DATA SYSTEMS: Reviews existing data systems and architectures to lead identification of areas for improvement and optimization.</li><li>DATA INFRASTRUCTURE: Oversees the preparation of data infrastructure to drive the efficient storage and retrieval of data.</li><li>DATA FORMATS: Reviews and resolves appropriate data formats to improve data usability and accessibility across the organization.</li><li>STAKEHOLDER MANAGEMENT: Partners collaboratively with multi-functional data and advanced analytic teams to capture requirements and ensure that data solutions meet the functional and non-functional needs of various partners.</li><li>DATA FRAMEWORKS: Builds complex prototypes to test new concepts and provides guidance to implement data engineering frameworks and architectures that improve data processing capabilities and support advanced analytics initiatives.</li><li>AUTOMATED DEPLOYMENT PIPELINES: Oversees the development of automated deployment pipelines improving efficiency of code deployments with fit for purpose governance.</li><li>DATA MODELING: Guides the team to perform data modeling in accordance to the datastore technology to ensure sustainable performance and accessibility.</li><li>TEAM MANAGEMENT: Manages team members to achieve the organizations goals, by ensuring productivity, communicating performance expectations, creating goal alignment, giving and seeking feedback, providing coaching, measuring progress and holding people accountable, supporting employee development, recognizing achievement and lessons learned, and developing enabling conditions for talent to thrive in an inclusive team culture.</li></ul> Requirements: <ul><li>Some experience in React, including hooks, context API, and state management libraries (Redux, Zustand)</li><li>Solid experience with JavaScript (ES6+), TypeScript, HTML5, CSS3.</li><li>Strong experience with C# and .NET Framework/.NET Core.</li><li>Solid understanding of object-oriented programming.</li><li>Experience with unit testing and mocking frameworks (e.g., xUnit, NUnit, Moq).</li><li>Familiarity with design patterns (e.g., Repository, Factory, Strategy).</li><li>Experience with SQL DBs, MySQL, Postgres, or MS SQL: writing queries, stored procedures, and performancetuning.</li><li>Knowledge of RESTful APIs and message-based systems.</li><li>Experience with relational databases and data access libraries (e.g., Entity Framework, Dapper).</li><li>Knowledge of ReactJS</li><li>Proficient in Git: branching, merging, pull requests, and version control workflows.</li></ul>

Responsibilities: <ul><li>Demonstrate accountability by ensuring timely and high-quality project delivery.</li><li>Be result driven, consistently achieving measurable outcomes and meeting goals.</li><li>Act as a team player, collaborating effectively with cross-functional teams.</li><li>Exhibit a can-do mentality, tackling challenges with a proactive and positive approach.</li><li>Apply software engineering patterns and principles to design, develop, test, integrate, maintain, troubleshoot and document complex and varied software solutions and incorporate security practices in newly developed and maintained applications.</li><li>Participate in peer code review sessions to review code, coach peers and ensure code quality.</li><li>Take the lead in the assigned agile team to adopt agile philosophies, facilitate agile ceremonies and identify continuous improvement opportunities.</li><li>Incorporate the companys engineering and development best practices within the full software development lifecycle including coding standards, code reviews, source control management, building processes, testing and security principles, to deliver high quality code rapidly.</li><li>Collaborate to lead demonstration and continuous feedback sessions to improve development and help drive the long term vision.</li><li>Participate in the engineering community by maintaining and sharing relevant technical approaches and modern skills.</li><li>Independently handle complex issues with minimal supervision, while escalating only the most complex issues to appropriate staff.</li><li>Other duties as assigned</li></ul> Requirements: <ul><li>Bachelor’s degree in a related field or equivalent experience</li><li>Confirmed experience automating pipelines for continuous integration, testing, delivery and security.</li><li>Confirmed experience architecting applications, databases, services or integrations.</li><li>Extensive experience with .NET Framework and .NET Core.</li><li>Familiarity with front-end technologies like HTML, CSS, JavaScript, and frameworks such as Angular, React, or Vue.js.</li><li>Experience with DevOps practices and tools such as Docker and CI/CD pipelines.</li><li>Knowledge of SQL Server and / or PostgreSQL for database design and management.</li><li>Hands-on experience with Kubernetes for container orchestration and management.</li><li>Experience with cloud platforms such as Azure, AWS, or GCP.</li><li>Minimum of 5 years of related work experience in software development using .NET technologies.</li></ul>

Responsibilities: <ul><li>Lead the design, development, and deployment of machine learning models using Python for applications including process optimization, predictive analytics, demand forecasting, computer vision, and decision support systems</li><li>Network across manufacturing, R&amp;D, commercial, and business functions to identify and collaboratively execute on high-impact challenges solvable through machine learning techniques</li><li>Build production-ready ML solutions within enterprise platforms (Databricks, Azure ML) including model training, endpoint deployment, and monitoring</li><li>Develop and maintain scalable, modular, well-documented code following MLOps best practices including version control, logging/metadata standards, and CI/CD principles</li><li>Lead code reviews, architectural discussions, and knowledge sharing to maintain high quality, production-ready deliverables across the team</li><li>Collaborate with domain experts and stakeholders across a wide variety of domains to understand needs, integrate ML solutions into workflows, and deliver measurable business value</li><li>Communicate complex machine learning methodology, results, and business impact clearly to technical and non-technical audiences through presentations, documentation, and visualizations</li><li>Stay current with advancements in machine learning, and apply them to solve business challenges</li><li>Mentor and provide technical guidance to data scientists, helping to grow the teams capabilities in Hyderabad</li><li>Support ML governance processes including architecture and methodology reviews, risk assessments, and compliance with enterprise standards</li><li>Lead project planning and execution, managing timelines, resources, and stakeholder expectations</li></ul> Requirements: <ul><li>Masters or Ph.D. in a relevant field such as Data Science, Statistics, Mathematics, Computer Science, Operations Research, or a domain-focused field (Engineering, Chemistry, Economics, Finance, etc.)</li><li>5-8+ years of experience in data science, machine learning, or a related field with demonstrated impact</li><li>Strong foundation in statistical and machine learning theory with proven ability to select, apply, and evaluate appropriate techniques for complex, real-world problems</li><li>Extensive hands-on experience building predictive models and deploying them to production environments using Python</li><li>Proficiency with modern ML frameworks (scikit-learn, XGBoost/CatBoost, TensorFlow/PyTorch) and data manipulation libraries (pandas, polars, numpy)</li><li>Experience with solution architecting and design for ML systems</li><li>Hands-on experience with cloud platforms such as Azure and/or Databricks for deploying and managing ML models and solutions</li><li>Experience with version control (Git), collaborative development, and code review processes</li><li>&nbsp;Strong written and oral communication skills with ability to present technical concepts and results to diverse audiences from technical teams to business leadership</li><li>History of project leadership and driving initiatives from conception to production</li><li>Strong problem-solving skills and the ability to work independently as well as lead and mentor team members&nbsp;</li></ul>

Responsibilities: <ul><li>Design, develop, and maintain robust pipelines to collect, transform, and store data used in model monitoring workflows (e.g., scoring data, performance metrics, outcomes).</li><li>Build scalable data architectures to support real-time and batch monitoring, including data ingestion, enrichment, and retention practices.</li><li>Develop reusable monitoring components (e.g., performance drift detectors, threshold-based alerts, metric repositories) that support various model types and regulatory needs.</li><li>Integrate data pipelines with model lifecycle platforms, MLOps tools, and observability solutions to ensure seamless model performance tracking.</li><li>Partner with model risk and compliance teams to ensure data lineage, audit trails, and documentation are preserved and accessible for regulatory reviews (e.g., SR 11-7 compliance).</li><li>Collaborate with data scientists, model validators, and product managers to align monitoring data infrastructure with evolving model monitoring requirements.</li><li>Work closely with the model monitoring analytics and strategy monitoring analytics teams within MO&amp;A to ensure the monitoring data infrastructure adapts to changing analytics and monitoring needs. Enable visualization and reporting capabilities through dashboards (e.g., Power BI, Tableau) that summarize model health, stability, and issue alerts.</li><li>Designing and maintaining high-performance data pipelines that ingest, transform, and version datasets for Model and Strategy Monitoring</li><li>Optimize data storage and compute performance for large-scale monitoring use cases involving high-frequency scoring or model ensembles.</li></ul> Requirements: <ul><li>Bachelor’s degree in a quantitative, technical, or data-focused field (e.g., Statistics, Mathematics, Computer Science, Data Science, Engineering) with 5+ years’ experience or in lieu of degree, and 7+ years of relevant work experience in, data engineering or related roles in the financial services or regulated analytics domain.</li><li>Strong proficiency with data engineering tools and frameworks (e.g., Apache Spark, Airflow, Kafka, dbt, PySpark).</li><li>Proficient in programming languages such as SAS, Python, and SQL for building monitoring pipelines and validation checks.</li><li>Experience with cloud-based data infrastructure (e.g., AWS, Azure, GCP) and data warehousing (e.g., Snowflake, Redshift, BigQuery).</li><li>Familiarity with MLOps practices, model metadata tracking (e.g., MLflow), and monitoring toolkits (e.g., Evidently AI, Why Labs, Prometheus).</li><li>Understanding of model risk governance requirements and the role of data engineering in ensuring compliant model monitoring.</li><li>Ability to work in an agile environment and deliver high-quality, production-grade code in collaboration with DevOps and platform engineering teams.</li></ul>

Role Description: <ul><li>SOFTWARE DEVELOPMENT: Partners to design and develop high quality software solutions by writing clean, maintainable and efficient codes.</li><li>AUTOMATION: Applies internal software deployment platform, methodologies and tools to automate the deployment process, ensuring smooth and reliable releases.</li><li>COLLABORATION: Partners with cross functional team of product managers, designers and other engineers to gather moderately complex requirements and deliver solutions that meet business needs.</li></ul> Responsibilities: <ul><li>SOFTWARE DEVELOPMENT: Partners to design and develop high quality software solutions by writing clean, maintainable and efficient codes.</li><li>AUTOMATION: Applies internal software deployment platform, methodologies and tools to automate the deployment process, ensuring smooth and reliable releases.</li><li>COLLABORATION: Partners with cross functional team of product managers, designers and other engineers to gather moderately complex requirements and deliver solutions that meet business needs.</li><li>TESTING &amp; DEBUGGING: Writes and maintains moderately complex unit tests and integration tests, and performs debugging to maintain the quality and performance of the software.</li><li>CONTINUOUS IMPROVEMENT: Makes suggestions for improving the software development and deployment processes, and implements the approved standards to improve efficiency and reliability.</li><li>DOCUMENTATION: Drafts and maintains comprehensive documentation for moderately complex software applications, deployment processes and system configurations.</li><li>TECHNICAL SUPPORT: Provides technical support and troubleshooting for moderately complex issues with deployed applications to ensure minimal downtime and fast resolution.</li></ul> Requirements: <ul><li>Software Engineer: Minimum requirement of 3 years of relevant work experience.&nbsp;</li><li>React/Typescript/UI</li><li>Java/Spring Boot/Python/APIs</li><li>PostgreSQL</li><li>CI/CD pipelines</li><li>AWS/Azure Cloud Services</li></ul>

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36 sql mastery Jobs found

Data Analyst

Software Engineer AI

AVP, Digital Analytics

Senior Data Scientist III - Agentic AI

SAP Basis Engineer

Manager, Data Engineering

Sr. Software Engineer - Ag & Trading .Net

Senior Data Scientist - Machine Learning

AVP – Model Ops Engineer

Software Engineer

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36 sql mastery Jobs found

Data Analyst

Software Engineer AI

AVP, Digital Analytics

Senior Data Scientist III - Agentic AI

SAP Basis Engineer

Manager, Data Engineering

Sr. Software Engineer - Ag & Trading .Net

Senior Data Scientist - Machine Learning

AVP – Model Ops Engineer

Software Engineer

WhiteCrow WhiteCrow is on a mission to connect companies with the best talent across the globe.

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