Job/Soft skill training

Health insurance

Professional degree assistance

Cafeteria

Free meal

Office cab/shuttle

Decimal Point Analytics Reviews by Employees

Research Analyst

Senior Research Analyst

Software Development Engineer

Team Lead

Data Research Analyst

Business Analyst

Senior Business Analyst

Data Scientist

Research Associate

Software Developer

Senior Manager

Analyst

Data Analyst

What You'll Do : - Data Engineering & Pipeline Development : Design, develop, and maintain scalable and robust ETL (Extract, Transform, Load) processes and data pipelines to ingest, transform, and load large datasets from diverse sources. - Data Modeling : Lead the design and implementation of conceptual, logical, and physical data models for various data initiatives, ensuring data integrity, efficiency, and optimal performance for analytical purposes. - Data Warehousing : Architect, implement, and manage enterprise data warehousing solutions, ensuring they effectively support business intelligence, reporting, and advanced analytics requirements. - Data Analytics Support : Collaborate closely with data analysts and data scientists to understand their needs, provide clean and structured data, and contribute to the development of analytical solutions. - Database Management : Work proficiently with various database technologies, including both SQL and NoSQL databases. - Big Data Technologies : Leverage your experience with big data technologies like Hadoop and Spark to process and manage large volumes of data. - Problem-Solving : Apply strong analytical and problem-solving skills to diagnose complex data issues, optimize data processes, and ensure data accuracy and reliability. What We're Looking For : - Experience : 5-8 of hands-on experience as a Data Engineer or in a similar data-focused role. - Data Engineering Expertise : Proven expertise in Data Engineering principles and practices. - Data Modeling : Strong proficiency in Data Modeling techniques. - ETL Mastery : Extensive experience in designing, developing, and optimizing ETL (Extract, Transform, Load) processes. - Data Warehousing : In-depth knowledge and hands-on experience with Data Warehousing concepts and solutions. - Data Analytics : Demonstrated proficiency in Data Analytics concepts and supporting analytical efforts. - Database Technologies : Proficient in database technologies, including SQL (e.g., MySQL, PostgreSQL, SQL Server) and NoSQL (e.g., MongoDB, Cassandra). - Big Data Ecosystem : Hands-on experience with big data technologies like Hadoop and Spark. - Problem-Solving : Strong analytical and problem-solving skills with a keen eye for detail. - Communication : Excellent communication and collaboration skills to work effectively within cross-functional teams. - Education : Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.

Chennai

Decision Point - Senior Data Engineer - Spark/Hadoop (5-8 yrs)

Company Description Decision Point Analytics develops analytics & big data solutions for CPG, Retail & Consumer-focused industries and works with global fortune 500 clients. They provide analytical insights & solutions that help develop sales & marketing strategies in the Retail & CPG Industry. Decision Point Analytics is founded by Ravi Shankar along with his classmates from IIT Madras with diverse experience across the CPG & Marketing Analytics domain. Role Description This is a full-time Lead Data Engineer (GCP) role based in Gurugram. The Lead Data Engineer will be responsible for data engineering tasks such as data modeling, ETL processes, data warehousing, and data analytics on-site. Job Title: Lead Data Engineer (GCP) Location: Gurugram, India (On-site, Full-time) Experience Level: Minimum 5 Years in Data Engineering About the Role: We are seeking a highly skilled and experienced Lead Data Engineer with a strong specialization in Google Cloud Platform (GCP) to join our team in Gurugram. This is a full-time, on-site role that offers the opportunity to lead complex data initiatives, architect scalable data solutions, and drive data-driven decision-making within the organization. The ideal candidate will have a robust background in data engineering principles, exceptional problem-solving abilities, and proven expertise in building and optimizing data pipelines and warehouses on GCP. Key Responsibilities: - Data Architecture & Design: Lead the design and implementation of robust, scalable, and efficient data architectures and data models, specifically optimized for GCP services. - ETL/ELT Pipeline Development: Design, develop, and maintain complex ETL (Extract, Transform, Load) or ELT pipelines to ingest, transform, and load data from various sources into data warehouses and data lakes on GCP. - Data Warehousing: Lead the development, optimization, and management of cloud-based data warehouses (e.g., Google BigQuery), ensuring data integrity, performance, and accessibility for analytical purposes. - GCP Expertise: Leverage deep proficiency in key GCP data services such as BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub, Composer (Apache Airflow), and others as relevant. - Big Data Technologies: Apply experience with Big Data technologies to process and manage large datasets efficiently. - Data Analytics & Insights: Collaborate with data scientists, analysts, and business stakeholders to understand data requirements, enable data analytics, and support the generation of actionable insights. - Performance Optimization: Continuously monitor and optimize data pipeline performance, data storage costs, and query efficiency on GCP. - Data Quality & Governance: Implement and enforce data quality checks, data governance policies, and best practices to ensure data reliability and accuracy. - Mentorship & Leadership: Provide technical guidance and mentorship to junior and mid-level data engineers, promoting best practices and fostering a culture of excellence. - Problem-Solving: Troubleshoot and resolve complex data-related issues, ensuring data availability and reliability. - Continuous Improvement: Stay abreast of the latest developments in GCP data services, data engineering tools, and methodologies, and advocate for their adoption. Qualifications : - Minimum 5 Years into Data Engineering - Data Engineering and Data Modeling skills - Experience with ETL (Extract Transform Load) processes - Data Warehousing knowledge - Strong background in Data Analytics - Proficiency in GCP (Google Cloud Platform) - Experience working with Big Data technologies - Excellent problem-solving and analytical abilities - Bachelor's or Master's degree in Computer Science or related field

Gurgaon/Gurugram

Decision Point - Lead Data Engineer - Google Cloud Platform (5-7 yrs)

Role : Data Architect - DataVault & Financial Services Focus Experience : 6+ Years (including 2+ years in DataVault Modeling) Location : Noida, Uttar Pradesh, India (Based on contextual understanding of your usual location) Are you a seasoned Data Architect with a passion for building robust and scalable data solutions, particularly within the financial services domain? Do you possess a deep understanding of DataVault 2.0 modeling and have a proven track record of leveraging data to drive business insights? If so, we have an exciting opportunity for you to join our team. Responsibilities : - Design and implement scalable and efficient data warehouse solutions based on the DataVault 2.0 methodology. - Develop and maintain conceptual, logical, and physical data models, with a strong emphasis on DataVault principles. - Collaborate with business stakeholders and data analysts to understand data requirements and translate them into robust data architecture designs. - Architect data integration processes using ETL/ELT tools and cloud data platforms (e.g., Azure Synapse, AWS Redshift, GCP BigQuery). - Utilize data modeling tools, preferably Wherescape 3D and RED, to create and manage DataVault models. - Ensure data quality, integrity, and security across the data warehouse environment. - Optimize data models and query performance for efficient data retrieval and analysis. - Provide guidance and best practices for data modeling and data warehousing to development teams. - Design and implement data visualization solutions, creating insightful dashboards and reports using tools like Power BI, Tableau, or Qlik. - Stay abreast of the latest trends and technologies in data architecture, data warehousing, and data modeling. - Contribute to the development of data governance policies and standards. - Effectively communicate complex data concepts and architectural designs to both technical and non-technical audiences. - Apply your understanding of key financial metrics and regulatory requirements (e.g., Basel III, SOX compliance) to design data solutions that meet industry standards. Experience : - 6+ years of experience in data analysis, with at least 2+ years of experience in DataVault modeling. - Prior experience in the financial services domain is highly preferred. Technical Skills : - Strong proficiency in SQL and hands-on experience with DataVault 2.0 methodology. - Familiarity with data analysis tools like Python, R, or SAS. - Experience with ETL/ELT tools and cloud data platforms (e.g., Azure Synapse, AWS Redshift, or GCP BigQuery). - Knowledge of Wherescape 3D and RED for modelling DataVault. Data Visualization : - Proficiency in creating dashboards and reports using tools such as Power BI, Tableau, or Qlik. Soft Skills : - Excellent analytical thinking and problem-solving abilities. - Strong communication skills to effectively collaborate with technical and non-technical stakeholders. Knowledge of Financial Services : - Understanding of key financial metrics and regulatory requirements, such as Basel III or SOX compliance.

Noida

Decision Point - Data Architect (6-7 yrs)

About Decision Point : Decision Point is a leading analytics consulting firm, enabling global Fortune 500 companies to drive decisions using advanced data solutions. We focus on innovation, impact, and empowering businesses through data-driven transformation. Key Responsibilities : - Lead the design and implementation of robust data pipelines and scalable data architectures. - Drive the adoption of Data Vault modeling and data warehousing best practices. - Develop and maintain ETL workflows using Python, SQL, and Azure Data Factory. - Collaborate with cross-functional teams to gather requirements and deliver high-quality data solutions. - Build data products and integrations on Azure using Databricks, Delta Lake, and other cloud-native tools. - Translate complex business problems into efficient data models. - Ensure data quality, governance, and performance tuning across platforms. - Guide and mentor a team of data engineers and analysts. Must-Have Skills : - Strong proficiency in Python and SQL. - Expertise in Data Vault modeling and Data Warehousing. - Hands-on experience with Azure Cloud Services (Data Factory, ADLS, Synapse, etc.) - Experience with Databricks and large-scale distributed computing. - Proven ability to design and manage ETL pipelines. - Knowledge of data visualization tools (Power BI/Tableau). - Excellent communication and leadership skills. Preferred Qualifications : - Prior experience in consulting or analytics-driven environments. - Strong understanding of CI/CD and DevOps practices for data platforms. - Exposure to ML workflows and advanced analytics is a plus.

Decision Point - Lead Data Engineer - Python/SQL (5-7 yrs)

Key Responsibilities : Data Testing Automation : - Design, implement, and manage automated testing frameworks for data pipelines, ETL/ELT processes, and data warehouses. - Develop test cases and scripts to validate data accuracy, transformations, and end-to-end workflows. - Integrate automated testing into CI/CD pipelines to ensure continuous quality monitoring. Data Quality (DQ) Assurance : - Define and implement data quality standards, metrics, and validation rules to ensure high data reliability. - Identify, monitor, and resolve data quality issues across diverse data sources. - Collaborate with data engineering teams to enforce data governance and quality standards. Synthetic Data Generation : - Use synthetic data generation tools and techniques to create datasets that mimic real-world scenarios. - Ensure privacy-compliant synthetic data generation while maintaining data realism for testing. Azure and MS Fabric Expertise : - Leverage Azure MS Fabric for data management and analysis during testing workflows. - Test and validate data pipelines and workflows within Azure Data Factory, Synapse Analytics, and other Azure services. DataVault Methodology : - Validate and test DataVault 2 - Collaborate with data engineers to implement automated validation of DataVault structures and transformations. Performance Optimization and Troubleshooting : - Debug and optimize testing processes for large-scale data systems. - Ensure that data pipelines and workflows meet performance, reliability, and scalability requirements. Documentation and Best Practices : - Maintain comprehensive documentation for testing frameworks, methodologies, and results. - Establish and promote best practices for data testing, quality assurance, and governance. Qualifications and Skills : Experience : - 6+ years in data quality assurance and testing roles with a focus on automation. - Proven expertise in DataVault 2.0 methodology testing and validation. Technical Skills : - Proficiency in scripting languages such as Python, Java, or similar for test automation. - Strong SQL skills for data validation and querying across complex datasets. - Hands-on experience with tools like Great Expectations, DataFold, or other data testing frameworks. Cloud and Platform Expertise : - Extensive experience working with Azure services, including Azure Data Factory, Synapse Analytics, and Data Lake. - Familiarity with Azure MS Fabric for data workflows and pipeline validation. Synthetic Data Tools : - Experience using synthetic data generation platforms such as Tonic.ai, Mockaroo, or custom-built tools. Soft Skills : - Excellent problem-solving and analytical skills. - Strong communication skills to articulate testing strategies and results to technical and non-technical stakeholders. Preferred Qualifications : - Certifications in Azure (e.g, Azure Data Engineer or Azure Solutions Architect). - Experience with metadata management and data governance tools. - Familiarity with performance testing tools for data pipelines in Azure. - Knowledge of CI/CD tools like Azure DevOps, Jenkins, or GitLab.

Decision Point - Quality Assurance Lead - Data Vault Testing (6-7 yrs)

Role Overview : As a Senior Data Engineer, you will be responsible for designing, developing, and managing scalable data solutions. You will work with modern data platforms like Databricks, Snowflake, and Azure Synapse, ensuring efficient data processing and automation. Key Responsibilities : - Lead end-to-end Data Engineering projects and implement Unified Data Platforms in production. - Design data models and architectures while optimizing data pipelines using DBT (nice to have). - Work with Big Data tools (Hive, Spark) and ETL platforms (Azure Data Factory, AWS Glue, Airflow, etc. - Ensure code versioning, CI/CD integration, and maintain DevOps practices (nice to have). - Collaborate with cross-functional teams to enhance data management, automation, and analytics. - Mentor team members and communicate effectively with stakeholders. Requirements : - Expertise in Python & SQL, with strong Data Warehousing & ELT experience. - 3+ years of experience with Big Data tools and cloud platforms. - Hands-on experience in Snowflake or Databricks (certification preferred). - Experience with data ingestion from APIs, ERP systems, and RDBMS. - Strong problem-solving, troubleshooting, and communication skills

Decision Point - Senior Data Engineer - Hive/Spark (3-4 yrs)

Job Description : What would a typical day at your work be like? - You will lead and manage the delivery of projects and be responsible for the delivery of project and team goals. - Build & support data ingestion and processing pipelines. - This will entail extract, load and transform of data from a wide variety of sources using latest data frameworks and technologies. - Design, build, test, and maintain machine learning infrastructure and frameworks to empower data scientists to rapidly iterate on model development. - Own and lead client engagement and communication on technical projects. - Define project scopes and track project progress and delivery. - Plan and execute project architecture and allocate work to team. - Keep up to date with advances in big data technologies and run pilots to design the data architecture to scale with the increased data volume. - Partner with software engineering teams to drive completion of multi-functional projects. What Do We Expect?. - Minimum 6 years of overall experience in data engineering and 2+ years leading a team as team lead and doing project management. - Experience working with a global team and remote clients. - Hands on experience in building data pipelines on various infrastructures. - Knowledge of statistical and machine learning techniques. - Hands on experience in integrating machine learning in data pipelines. - Ability to work hands-on with the data engineers in the team in design and development of the solution using the relevant big data technologies and data warehouse concepts Strong knowledge of advanced SQL, data warehousing concepts, DataMart designing. - Have strong experience in modern data platform components such as Spark, Python, etc. - Experience with setting up and maintaining Data warehouse (Google BigQuery, Redshift, Snowflake) and Data Lakes (GCS, AWS S3 etc.) for an organization. - Experience in building data pipeline with AWS Glue, Azure Data Factory and Google Dataflow. - Experience with relational SQL and NoSQL databases, including Postgres and Cassandra / MongoDB. - Strong problem solving and communication skills.

Decision Point - Lead Data Engineer - Python/Spark (6-7 yrs)

What would a typical day at your work be like ? - Project Lifecycle Management : Proactively manage the entire project lifecycle, from conceptualization to delivery, ensuring thorough planning, execution, and quality outcomes. - Tool Development Ownership : Take full ownership of tool development by defining business objectives, understanding datasets, and creating insightful wireframes to drive project success. - Project Delivery Commitment : Commit to delivering a minimum of four projects within the evaluation year, addressing challenges proactively to ensure on-time delivery and quality outcomes. - Technical Understanding and Optimization : Develop a comprehensive understanding of tools developed and their data flow, actively seeking opportunities for optimization and improvement to enhance project efficiency. - Quality Assurance and Closure : Ensure timely and quality delivery of tools, actively seeking confirmation of project closure via email and addressing any outstanding issues to maintain client satisfaction. - Client Interaction and Business Repetition : Foster strong client relationships by actively engaging with them to understand their needs, identify opportunities for repeat business, and ensure alignment of project deliverables with client expectations. - Team Collaboration and Problem-Solving : Facilitate effective team collaboration by enabling detailed meeting notes, focusing on clear discussion points and action items, and proactively contributing to problem-solving to overcome challenges. - Stakeholder Engagement : Partner closely with cross-functional teams, including data engineering, consultants, and data science, to plan, execute, and deliver projects that exceed client expectations. - Quality Control and Documentation : Uphold the highest standards of project quality by implementing rigorous quality checks and maintaining. An ideal candidate will be/have : - End-to-End Project Management : Successfully manage technical projects throughout the Software Development Life Cycle (SDLC), ensuring seamless navigation through various stages. - Data Visualization and Analysis : Utilize Power BI proficiency to create interactive reports and dashboards, extracting valuable insights from data for informed decision-making. - Cloud Integration and Management : Apply hands-on experience with Azure and AWS to effectively leverage cloud platforms for project development and deployment. - Advanced Data Analysis : Utilize advanced Excel skills for data analysis, modeling, and reporting, including complex formulae and pivot tables. - Analytical Insight Generation : Apply strong analytical skills to interpret data, identify trends, and draw actionable insights to drive project.

Others

Decision Point - Technical Project Manager (3-8 yrs)

Apply to 8 Decimal Point Analytics jobs on Hirist.tech, India's No.1 IT job portal. Explore IT job in Decimal Point Analytics Now! 

Jobs by location

Decimal Point Analytics Jobs

Explore Jobs based in trending companies