Jobs at v4c.ai

Build and optimize production batch and streaming data pipelines on Databricks using PySpark, SQL, Spark, and Delta Lake. Responsibilities include implementing Medallion architecture, deploying cloud data resources, automating validation, tuning performance, structuring enterprise datasets, and troubleshooting production issues. The role requires end-to-end ownership of enterprise data platforms, legacy warehouse migrations, data modeling, CI/CD, DevOps, and client-facing collaboration.

Lead customer-facing data engineering engagements from architecture through production handover. Build and optimize batch and streaming Lakehouse pipelines on Databricks using PySpark, SQL, and Delta Lake. Establish Medallion architecture conventions, implement data quality and automated testing, optimize Spark performance and platform costs, and mentor engineers. Partner with architects and explain technical trade-offs to customers.

Own enterprise Databricks Lakehouse target architectures, including ingestion, storage, transformation, serving, governance, cost, and performance. Lead legacy platform assessments and migration strategies, establish data modeling and Unity Catalog standards, guide technical estimates and pre-sales discovery, mentor engineers, review designs, and create reusable architecture assets. The role requires extensive data architecture experience, cloud migration expertise, Python, SQL, Spark, CI/CD, and customer-facing technical leadership.

Own quality strategy and release sign-off for enterprise Databricks data platforms. Build Python and PySpark test suites with pytest, validate ingestion and Medallion transformations, implement data quality rules, test Delta Lake and ELT workflows, and maintain CI quality gates that block failed releases. Partner with data engineers and customers to improve automation coverage and prevent production data errors.

Own enterprise Databricks Lakehouse target architectures, including ingestion, storage, transformation, serving, governance, cost, and performance. Lead legacy platform assessments and migration strategies, define data modeling and Unity Catalog standards, guide engineering teams, support scoping and pre-sales, review designs, mentor senior engineers, and create reference architectures. Requires 8+ years of data architecture or engineering experience and expertise with Python, SQL, Spark, cloud migrations, CI/CD, and modern Lakehouse platforms.

Senior Business Analyst bridging healthcare business needs and technology teams. Responsibilities include gathering requirements, facilitating stakeholder sessions, creating business and data documentation, supporting EDW data integration and quality initiatives, performing gap analysis, assisting UAT, and ensuring healthcare data governance and regulatory compliance.

Builds and maintains scalable data pipelines, data lakes, and warehouses supporting analytics and AI/ML workflows. The role involves Databricks platform engineering, feature engineering, MLOps integration, cluster optimization, data governance, security, and collaboration with data scientists and analysts to productionize machine learning models.

Designs and optimizes scalable data platforms, architectures, and production pipelines supporting AI and machine learning. Responsibilities include Databricks Lakehouse development, batch and real-time processing, data governance, security, monitoring, performance tuning, infrastructure planning, and collaboration with DevOps, MLOps, and cross-functional teams. The role serves as the AI team’s data engineering subject matter expert and ensures reliable, secure, cost-effective data solutions.

Manage the complete sales cycle including lead generation, networking, and deal closure in a remote team environment. Work with Databricks professionals to drive new business.

Design, develop, and maintain large-scale data systems and scalable ETL pipelines. Implement data models, work with Hadoop/Spark/PySpark/Kafka, troubleshoot and optimize data systems, and upskill into Databricks and AI/ML projects.

Manage full-cycle recruiting for technical and business roles: source candidates, screen and assess, coordinate interviews, conduct reference checks, partner with hiring managers, maintain ATS records, track recruiting metrics, and improve hiring processes to ensure a positive candidate experience and efficient hiring.

Support project planning, coordination, and execution across cross-functional teams. Assist with timelines, task tracking, documentation, meeting preparation, status reporting, and basic metrics. Monitor progress, flag risks, and help ensure deliverables meet quality standards. Participate in training and collaboration days while learning project management tools and methodologies to grow professionally.

Entry-level data engineering role with structured training. Collaborate with senior engineers to design and build data pipelines, work with real-world datasets and cloud platforms, and gain hands-on experience during an initial on-site training period in Pune.

Entry-level role for fresh graduates to support data collection, cleaning, exploratory analysis, and implementation of machine learning models. Collaborate with a data science team, document findings, and participate in training while working from the Pune office during initial onboarding.