Brillio 2

<div><u><strong>Data Specialist</strong></u></div>
<h2>Primary Skills</h2><div>
<div>
<h2>Databricks Engineer</h2>
<h2>Role Overview</h2>
<p>We are seeking a highly skilled <strong>Databricks Engineer</strong> to design, develop, and optimize scalable data engineering and analytics solutions on the <strong>Databricks Lakehouse Platform</strong>. The ideal candidate will possess strong expertise in <strong>Databricks, PySpark, and SQL</strong>, with hands-on experience building batch and real-time data pipelines, implementing Lakehouse architectures, and ensuring data governance and performance optimization.</p>
<h2>Key Responsibilities</h2>

<li>Design, develop, and maintain end-to-end data pipelines using <strong>Databricks</strong> and <strong>PySpark</strong>.</li>
<li>Build and implement <strong>Lakehouse architectures</strong> utilizing Bronze, Silver, and Gold data layers.</li>
<li>Develop and manage <strong>Delta Lake</strong> solutions with ACID transactions, schema enforcement, and data reliability features.</li>
<li>Create, monitor, and optimize <strong>Delta Live Tables (DLT)</strong> pipelines.</li>
<li>Implement scalable and efficient data ingestion processes using <strong>Auto Loader</strong>.</li>
<li>Develop and manage real-time data processing solutions using <strong>Structured Streaming</strong>.</li>
<li>Orchestrate, schedule, and monitor data workflows using <strong>Databricks Workflows</strong>.</li>
<li>Design and implement Lakehouse data models to support reporting, analytics, and business intelligence requirements.</li>
<li>Establish and enforce data governance, security, and access controls using <strong>Unity Catalog</strong>.</li>
<li>Optimize Spark jobs, SQL queries, and overall platform performance to ensure efficiency and scalability.</li>
<li>Collaborate with cross-functional teams, including data analysts, architects, and business stakeholders, to deliver high-quality data solutions.</li>

<h2>Required Skills (Must Have)</h2>

<li>Databricks Platform

</li><li>Delta Lake</li>
<li>Delta Live Tables (DLT)</li>
<li>Unity Catalog</li>
<li>Databricks Workflows</li>


<li>PySpark and Apache Spark</li>
<li>Structured Streaming</li>
<li>Auto Loader</li>
<li>SQL</li>
<li>Lakehouse Data Modeling</li>
<li>Strong understanding of data engineering best practices and scalable data architectures</li>

<h2>Preferred Skills (Good to Have)</h2>
<h3>Azure Ecosystem</h3>

<li>Azure Data Factory (ADF)</li>
<li>Azure Synapse Analytics</li>
<li>Microsoft Purview</li>
<li>Microsoft Fabric</li>

<h3>AWS Ecosystem</h3>

<li>AWS Glue</li>
<li>AWS Lambda</li>
<li>AWS Step Functions</li>

<h3>Data Engineering &amp; Integration</h3>

<li>Apache Airflow</li>
<li>DBT</li>
<li>Fivetran</li>
<li>Informatica</li>

<h3>Streaming &amp; Analytics</h3>

<li>Apache Kafka</li>
<li>Power BI</li>

<h3>Data Governance</h3>

<li>Collibra</li>
<li>Alation</li>

<h3>GCP</h3>

<li>BigQuery</li>

<h2>Qualifications</h2>

<li>Bachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or a related discipline.</li>
<li>Proven experience in designing and implementing cloud-based data engineering solutions and scalable data pipelines.</li>
<li>Strong analytical, troubleshooting, and problem-solving capabilities.</li>
<li>Experience working in agile and collaborative environments.</li>
<li>Excellent communication and stakeholder management skills.</li>

<h2>Preferred Candidate Profile</h2>

<li>Hands-on experience with modern Lakehouse architectures and enterprise-scale data platforms.</li>
<li>Strong understanding of data governance, security, and compliance frameworks.</li>
<li>Experience delivering both batch and real-time data processing solutions.</li>
<li>Ability to work independently while collaborating effectively across global teams.</li>

<h3>Key Technologies</h3>
<p><strong>Databricks | PySpark | Apache Spark | Delta Lake | Delta Live Tables (DLT) | Unity Catalog | Structured Streaming | Auto Loader | SQL | Lakehouse Architecture | Azure | AWS | Airflow | Kafka | Power BI</strong></p>
</div>
</div>
<h2>Specialization</h2><ul><li>Databricks Engineering: Lead Data Engineer</li></ul>
<h2>Job requirements</h2><div>
<div>
<h2>Databricks Engineer</h2>
<h2>Role Overview</h2>
<p>We are seeking a highly skilled <strong>Databricks Engineer</strong> to design, develop, and optimize scalable data engineering and analytics solutions on the <strong>Databricks Lakehouse Platform</strong>. The ideal candidate will possess strong expertise in <strong>Databricks, PySpark, and SQL</strong>, with hands-on experience building batch and real-time data pipelines, implementing Lakehouse architectures, and ensuring data governance and performance optimization.</p>
<h2>Key Responsibilities</h2>

<li>Design, develop, and maintain end-to-end data pipelines using <strong>Databricks</strong> and <strong>PySpark</strong>.</li>
<li>Build and implement <strong>Lakehouse architectures</strong> utilizing Bronze, Silver, and Gold data layers.</li>
<li>Develop and manage <strong>Delta Lake</strong> solutions with ACID transactions, schema enforcement, and data reliability features.</li>
<li>Create, monitor, and optimize <strong>Delta Live Tables (DLT)</strong> pipelines.</li>
<li>Implement scalable and efficient data ingestion processes using <strong>Auto Loader</strong>.</li>
<li>Develop and manage real-time data processing solutions using <strong>Structured Streaming</strong>.</li>
<li>Orchestrate, schedule, and monitor data workflows using <strong>Databricks Workflows</strong>.</li>
<li>Design and implement Lakehouse data models to support reporting, analytics, and business intelligence requirements.</li>
<li>Establish and enforce data governance, security, and access controls using <strong>Unity Catalog</strong>.</li>
<li>Optimize Spark jobs, SQL queries, and overall platform performance to ensure efficiency and scalability.</li>
<li>Collaborate with cross-functional teams, including data analysts, architects, and business stakeholders, to deliver high-quality data solutions.</li>

<h2>Required Skills (Must Have)</h2>

<li>Databricks Platform

</li><li>Delta Lake</li>
<li>Delta Live Tables (DLT)</li>
<li>Unity Catalog</li>
<li>Databricks Workflows</li>


<li>PySpark and Apache Spark</li>
<li>Structured Streaming</li>
<li>Auto Loader</li>
<li>SQL</li>
<li>Lakehouse Data Modeling</li>
<li>Strong understanding of data engineering best practices and scalable data architectures</li>

<h2>Preferred Skills (Good to Have)</h2>
<h3>Azure Ecosystem</h3>

<li>Azure Data Factory (ADF)</li>
<li>Azure Synapse Analytics</li>
<li>Microsoft Purview</li>
<li>Microsoft Fabric</li>

<h3>AWS Ecosystem</h3>

<li>AWS Glue</li>
<li>AWS Lambda</li>
<li>AWS Step Functions</li>

<h3>Data Engineering &amp; Integration</h3>

<li>Apache Airflow</li>
<li>DBT</li>
<li>Fivetran</li>
<li>Informatica</li>

<h3>Streaming &amp; Analytics</h3>

<li>Apache Kafka</li>
<li>Power BI</li>

<h3>Data Governance</h3>

<li>Collibra</li>
<li>Alation</li>

<h3>GCP</h3>

<li>BigQuery</li>

<h2>Qualifications</h2>

<li>Bachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or a related discipline.</li>
<li>Proven experience in designing and implementing cloud-based data engineering solutions and scalable data pipelines.</li>
<li>Strong analytical, troubleshooting, and problem-solving capabilities.</li>
<li>Experience working in agile and collaborative environments.</li>
<li>Excellent communication and stakeholder management skills.</li>

<h2>Preferred Candidate Profile</h2>

<li>Hands-on experience with modern Lakehouse architectures and enterprise-scale data platforms.</li>
<li>Strong understanding of data governance, security, and compliance frameworks.</li>
<li>Experience delivering both batch and real-time data processing solutions.</li>
<li>Ability to work independently while collaborating effectively across global teams.</li>

<h3>Key Technologies</h3>
<p><strong>Databricks | PySpark | Apache Spark | Delta Lake | Delta Live Tables (DLT) | Unity Catalog | Structured Streaming | Auto Loader | SQL | Lakehouse Architecture | Azure | AWS | Airflow | Kafka | Power BI</strong></p>
</div>
</div>

Databricks Data Specialist - R01569707

<div><u><strong>Lead Data Engineer</strong></u></div>
<h2>Primary Skills</h2><ul><li>AKS, Event Hub, Azure DevOps, Azure sentinel, Cosmos DB, Azure Functions</li></ul>
<h2>Specialization</h2><ul><li>Azure Data Engineering Advanced: Senior Lead Data Engineer</li></ul>
<h2>Job requirements</h2><ul><li>Experience Range: with at least 4 to 6 years of hands-on experience in data engineering, including substantial expertise in Azure analytics and pipeline development Key Responsibilities: 1. Design and implement scalable ETL processes using Azure Data Factory, Synapse Analytics, and Spark, delivering robust real-time and batch data feeds 2. Develop and optimize data pipelines and models for large-scale lakehouse and warehouse analytics systems, achieving high performance and reliability targets 3. Integrate and manage data sources from Azure Data Explorer, Kusto, and Delta Lake formats to enable advanced analytics and reporting capabilities 4. Configure and maintain cloud-hosted data architectures, leveraging Azure services to ensure secure, efficient, and resilient data operations 5. Collaborate with cross-functional teams to capture data requirements and drive test-driven, data-driven development methodologies 6. Monitor, troubleshoot, and enhance SQL performance, ensuring efficient query execution and maintaining data quality across all systems 7. Develop and maintain Power BI reports, focusing on data modeling and DAX to deliver actionable insights to stakeholders 8. Document technical business processes and data architecture, supporting organizational knowledge sharing and compliance Required Skills: 1. Azure Synapse Analytics 2. Azure Data Factory 3. Azure Data Explorer (Kusto) 4. Pyspark 5. Delta Lake 6. SQL performance tuning 7. Power BI (Data Modeling and DAX) 8. Azure Data Lake Preferred Skills: 1. Azure DevOps for CI/CD 2. AKS (Azure Kubernetes Service) 3. Event Hub 4. Azure Sentinel 5. Experience with large-scale streaming analytics Desired Qualifications: 1. Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a closely related discipline 2. Microsoft Certified: Azure Data Engineer Associate 3. Microsoft Certified: Azure Solutions Architect Expert Additional Information: Location: Bangalore</li></ul>

Lead Data Engineer - R01569363

<div><u><strong>Lead Data Engineer</strong></u></div>
<h2>Primary Skills</h2><ul><li>ETL Fundamentals, SQL, SQL (Basic + Advanced), Python, Data Warehousing, Modern Data Platform Fundamentals, Data Modelling Fundamentals, PLSQL, T-SQL, Stored Procedures</li></ul>
<h2>Specialization</h2><ul><li>ETL Specialization: Data Engineer</li></ul>
<h2>Job requirements</h2><ul><li>Experience Range: 4 to 6 years of experience, including hands-on expertise in ETL and Informatica MDM solutions Key Responsibilities: 1. Design and implement robust ETL pipelines using Informatica MDM to ensure accurate data integration and management across enterprise systems 2. Develop, optimize, and maintain complex SQL, PLSQL, and T-SQL stored procedures for efficient data processing and transformation 3. Lead data modeling initiatives, translating business requirements into scalable data warehouse solutions and modern data platform architectures 4. Collaborate with cross-functional teams to gather requirements, validate data quality, and deliver actionable insights through advanced data engineering practices 5. Troubleshoot and resolve data-related issues, ensuring high availability, reliability, and performance of ETL workflows 6. Automate data ingestion and transformation tasks using Python, enhancing operational efficiency and reducing manual intervention 7. Monitor and improve ETL performance, proactively identifying bottlenecks and implementing enhancements to achieve optimal throughput 8. Ensure adherence to data governance policies and best practices, maintaining data integrity and security throughout the lifecycle Required Skills: 1. Informatica MDM 2. ETL pipeline development with Informatica 3. Advanced SQL programming 4. Python for data engineering 5. Enterprise data warehousing 6. Data modeling for large-scale systems 7. PLSQL 8. T-SQL 9. Stored procedures development Preferred Skills: 1. Experience with cloud-based ETL tools such as Azure Data Factory or AWS Glue 2. Expertise in master data management platforms beyond Informatica 3. Knowledge of big data technologies such as Spark or Hadoop 4. Familiarity with data governance frameworks 5. Proficiency in performance tuning and optimization of ETL processes Desired Qualifications: 1. Bachelor’s degree in Computer Science, Information Technology, or a related field 2. Relevant certifications in Informatica MDM or ETL technologies Additional Information: Informatica MDM skills are required as a core competency for this role.</li></ul>

Lead Data Engineer - R01568425

<div><u><strong>Senior Data Scientist</strong></u></div>
<h2>Primary Skills</h2><ul><li>Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio</li></ul>
<h2>Specialization</h2><ul><li>Data Science Advanced: Data Specialist</li></ul>
<h2>Job requirements</h2><ul><li>Technical Skills: Software Engineering (Backend Development using Python) Data Science and Analytics SQL and Data Analysis AI/ML experience, preferably with Agentic AI frameworks and implementations Experience integrating AI solutions into enterprise applications Preferred Profile: Strong problem-solving and analytical skills Experience working on end-to-end AI product development Exposure to Generative AI and AI Agent architectures would be highly beneficial</li></ul>

Senior Data Science Lead - R01567407

<div><u><strong>Data Scientist</strong></u></div>
<h2>Primary Skills</h2><ul><li>Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio</li></ul>
<h2>Specialization</h2><ul><li>Data Science Advanced: Data Specialist</li></ul>
<h2>Job requirements</h2><ul><li>AI Developer Anaplan Key Responsibilities AI Engineering &amp; Development • Build and deliver internal enterprise AI solutions — including LLM integrations, agentic AI workflows, Gemini Enterprise features, and supporting data pipelines — designed for the scale, security, and reliability requirements of a global workforce. • Develop code in Python following the AI engineering standards, coding practices, and technical guardrails. • Participate in technical design reviews and contribute implementation input during intake, design, and production deployment phases. • Evaluate and prototype new AI tools, frameworks, and platforms, providing input on findings to support technical recommendations. • Contribute to the development and maintenance of the LLM abstraction layer and multi-model integrations. • Support technical debt remediation, system observability, and scalability improvements for Anaplan’s internal AI systems. AI Platform Governance &amp; Enablement • Support the AI intake process by contributing technical assessments of feasibility, build complexity, and integration requirements for employee-facing tools. • Ensure all assigned internal AI initiatives meet engineering, security, compliance, and responsible AI standards before and during development. • Develop AI solutions against clear technical specifications • Build and maintain integrations across core enterprise systems (Salesforce, ServiceNow, Gainsight, Workday) as directed. • Implement human-in-the-loop, explainability, and auditability features as required for decision-impacting AI systems. • Support AI agent lifecycle management including monitoring, feedback loops, and continuous improvement. AI Operations &amp; Workforce Adoption • Contribute to the technical deployment and operationalization of AI agents and solutions, supporting production readiness and stability for Anaplan employees. • Support Gemini Enterprise adoption and participate in AI Friday sessions with technical demonstrations and knowledge sharing. • Help build and maintain engineering dashboards tracking internal AI system performance, adoption rates, and ROI metrics. • Communicate engineering decisions and solution outcomes clearly to technical peers and team leads. Innovation, R&amp;D &amp; Emerging Technology • Research and prototype emerging AI/ML techniques, large language models, agent frameworks, and enterprise tooling to identify internal productivity and cost-saving opportunities. • Contribute to proof-of-concept pilots that validate novel internal use cases before committing to full engineering investment. • Maintain and contribute to the AI innovation pipeline — a living backlog of high-potential experiments. Cross-Functional Technical Partnership • Work alongside the Transformation &amp; Financial Flexibility team to support identification and technical validation of AI-driven cost-saving opportunities in Anaplan’s internal operations. • Act as a technical contributor and AI subject matter resource to business units identifying internal AI use cases. • Build collaborative working relationships across Engineering, Security, Compliance, Legal, and HR. Required Qualifications • 5+ years of software or AI/ML engineering experience, with a focus on building and shipping production-grade AI or data-driven applications. • Hands-on experience with the AI/ML stack: LLMs, retrieval-augmented generation (RAG), agent frameworks, or MLOps pipelines — with exposure to enterprise or workforce deployment contexts. • Experience developing and deploying AI applications in cloud environments, including reliability, observability, and performance considerations. • Proficiency in Python and familiarity with enterprise cloud AI platforms (GCP Vertex AI, Azure AI, or AWS SageMaker) and AI orchestration tooling. • Exposure to Google Workspace AI and Gemini Enterprise, or equivalent enterprise AI platforms (e.g., Microsoft Copilot, OpenAI Enterprise). • Ability to work effectively within defined architectural standards and del</li></ul>

Brillio 2

Open jobs at Brillio 2

Databricks Data Specialist - R01569707

Lead Data Engineer - R01569363

Lead Data Engineer - R01568425

Senior Data Science Lead - R01567407

Senior Data Scientist - R01565738

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