Associate Director – Databricks Architect

Data Architect – Databricks & AWS

• Strong experience in Data Architecture, Data Engineering, Databricks, Apache Spark/PySpark, Python, and Advanced SQL.
• Design and implement scalable ETL/ELT pipelines, data platforms, and Lakehouse architectures using Medallion Architecture.
• Experience with Databricks, Databricks Workflows, Unity Catalog OR Databricks Jobs
• Strong knowledge of Delta Lake, Databricks Workflows, Delta Live Tables (DLT), and dimensional data modeling.
• Hands-on experience with AWS services such as S3, Glue, IAM, Lambda, and CloudWatch.
• Experience with Apache Airflow, Data Warehouse concepts, Git/CI-CD, performance optimization, and data quality.
• Good to have exposure to Kafka/Structured Streaming, Unity Catalog, and modern data governance.

8-12 years experience on Data Engineering role working with Databricks & Azure Cloud technologies.

• Bachelor’s degree in computer science, Information Technology, or related field.

• Strong proficiency in PySpark, Python, SQL.

• Strong experience in data modeling, ETL/ELT pipeline development, and automation • Hands-on experience with performance tuning of data pipelines and workflow

• Proficient in working on Azure cloud components Azure Data Factory, Azure DataBricks, Azure Data Lake etc.

• Experience with data modeling, ETL processes, Delta Lake and data warehousing.

• Experience on Delta Live Tables, Autoloader & Unity Catalog.

Preferred - Knowledge of the insurance industry and its data requirements.

• Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.

• Excellent communication and problem-solving skills to work effectively with diverse teams

• Excellent problem-solving skills and ability to work under tight deadlines. 

Lead Data Engineer – Databricks & Azure

• We are looking for a Lead Data Engineer with 5–7 years of experience in Azure, Databricks, and Microsoft Fabric to lead and mentor the data engineering team. The role involves implementing scalable data solutions using Databricks as the core platform, building modern data pipelines, and enabling seamless integration with Microsoft Fabric and Power BI.

Responsibilities & Requirements:

• Lead and mentor data engineers while driving hands-on technical delivery.
• Implement approved Databricks and Microsoft Fabric architectures and solutions.
• Develop metadata-driven bronze, silver, and gold data pipelines.
• Build reusable batch, incremental, CDC, and streaming ingestion pipelines.
• Strong hands-on experience with Azure Databricks, PySpark, SQL, and Delta Lake.
• Experience with Microsoft Fabric, OneLake, Direct Lake, and Power BI integration.
• Implement data quality checks, reconciliation, source-to-target mapping, and metadata management.
• Configure Unity Catalog for data governance, access control, auditing, and lineage.
• Implement CI/CD, automated deployments, and Datadog monitoring.
• Drive testing, documentation, operational readiness, and knowledge transfer.

Good to have:

• Experience with MDM and master data integration.
• Exposure to Azure DevOps or GitHub Actions.
• Experience with automated data testing and reconciliation frameworks.
• Experience working with onshore stakeholders and distributed engineering teams.
• Strong communication, problem-solving, and team leadership skills.

Solution Architect – AZURE Data Engineering

Job Overview

We are looking for an experienced Solution Architect – Data Engineering with strong expertise in designing data solutions and hands-on experience with Azure, Synapse, PySpark, Data Warehousing, and Data Lakes. The ideal candidate should have strong architectural and data engineering knowledge.

Key Responsibilities

• Design and implement scalable data architecture and solutions.
• Develop and manage Data Warehouse and Data Lake architectures.
• Design data platforms using Medallion Architecture.
• Lead Data Engineering and ETL activities.
• Work with Azure Synapse Analytics for data processing and analytics.
• Develop data solutions using PySpark / Apache Spark.
• Define and implement data validation and data quality processes.
• Collaborate with business, data, and technology teams to deliver effective data solutions.

Required Skills

• Strong experience in Solution Architecture / Data Architecture.
• Strong knowledge of Data Warehouse and Data Lake architecture.
• Good understanding of Medallion Architecture.
• Strong experience in Data Engineering and ETL.
• Hands-on experience with Microsoft Azure and Azure Synapse Analytics.
• Strong knowledge of PySpark / Apache Spark.
• Experience with Data Validation and Data Quality.
• Good communication and stakeholder management skills.
• 
  

Experience

8+ Years

Position: Technical Architect – Data Engineering

Job Summary:

• We are looking for an experienced Technical Architect to lead the design and implementation of modern cloud-based data platforms.
• The ideal candidate should have strong expertise in Azure and/or AWS, Databricks, Snowflake, modern data architecture, and large-scale data engineering.
• The candidate will work closely with business stakeholders, architects, and engineering teams to deliver scalable, secure, and high-performance data solutions.

Key Responsibilities

• Design enterprise-scale data lakehouse and data warehouse architectures.

• Define data ingestion, transformation, and serving architecture.

• Lead architecture discussions and technical governance.

• Design scalable ETL/ELT frameworks using Databricks and Snowflake.

• Define best practices for security, performance, CI/CD, and DevOps.

• Guide engineering teams on implementation.

• Collaborate with business, product, and data governance teams.

Must Have Skills :

• Azure or AWS

• Databricks

• Snowflake

• Data Lakehouse Architecture

• PySpark

• SQL

• Data Modelling

• ETL/ELT Architecture

• Performance Optimization

• CI/CD

• Terraform or Infrastructure as Code (preferred)

Preferred

• Insurance domain

• dbt

• Unity Catalog

• Delta Lake

• Iceberg

• Azure Data Factory / AWS Glue  

Technical Lead 

Job Description 

Role Overview 

We are seeking a highly skilled Data Engineering Lead with minimum of 5 years of hands-on experience in Azure data engineering, data warehousing, and automation-driven  integration. The ideal candidate should be a proven technical lead, capable of driving end-to-end 

project delivery and working closely with customer teams. This is a Work from Office / Customer Site role. 

Roles and Responsibilities 

• Lead the design, development, and delivery of data engineering and automation projects. 

• Architect and implement ETL/ELT pipelines using Azure Data Factory. 

• Design and manage enterprise data warehouses including dimensional modeling and 

schema optimization. 

• Manage Azure components including Storage Accounts, Data Lakes, Azure SQL, and Synapse. 

• Drive automation initiatives across data ingestion and transformation workflows. 

• Collaborate with business and technical stakeholders to translate requirements into scalable solutions. 

• Mentor team members and enforce engineering best practices. 

• Serve as the technical anchor responsible for ensuring high-quality, on-time delivery. 

Skills and Qualification 

• Strong hands-on experience with Azure Data Factory, Azure Storage, Azure SQL/Synapse. 

• Deep understanding of data warehousing concepts: star/snowflake schemas, fact/dimension modeling. 

• Experience with automation-led data engineering solutions. 

• Strong troubleshooting, optimization, and analytical skills. 

• Excellent communication and stakeholder management abilities. 

• Proven experience as a Technical Lead leading teams and delivery. 

Must Have 

• Min of 5 years of relevant data engineering experience. 

• Strong Azure Data Engineering and Data Warehousing expertise. 

• Proven Technical Lead experience. 

• Ability to work from office and customer site. 

• Strong ownership mindset with a focus on quality and delivery excellence.

Key Skills:

Python & System Design: Strong expertise in Python and system design, with proven experience architecting scalable, production-grade systems.

LLM & Agentic AI: Proven experience building LLM-based and agentic systems from the ground up and evaluating them based on reproducibility, calibration, and cost.

Cloud & Data Architecture: Deep understanding of data and cloud architecture across AWS and Azure, including multi-store data design, distributed systems, streaming, security, and cost optimization.

Azure Databricks: Hands-on production experience with Azure Databricks architecture, including Unity Catalog, Delta Lake, cluster policy design, job orchestration, and Terraform provisioning.

Supply Chain & Inventory Management: Strong domain knowledge of supply chain and inventory operations, including demand planning, MRP logic, inventory parameters, and translating business planning decisions into data requirements.

System Architecture & Technical Decision-Making: Ability to make sound architectural decisions based on first principles, evaluate technical trade-offs, and avoid unnecessary complexity and abstraction.

LLM & Agentic AI: Proven experience building LLM-based and agentic systems from the ground up and evaluating them based on reproducibility, calibration, and cost.

Technical Leadership: Ability to influence teams without direct authority, establish technical standards, prepare technical specifications and documentation, and mentor team members.

Experience in pharmaceutical or regulated industries is highly valued.

About Us:

The QX Impact was launched with a mission to make A.I accessible and affordable and deliver AI Products/Solutions at scale for the enterprises by bringing the power of Data, AI, and Engineering to drive digital transformation. We believe without insights; businesses will continue to face challenges to better understand their customers and even lose them. Secondly, without insights businesses won't’ be able to deliver differentiated products/services; and finally, without insights, businesses can’t achieve a new level of “Operational Excellence” is crucial to remain competitive, meeting rising customer expectations, expanding markets, and digitalization.

Job Summary:

We are looking for a Senior Data Engineer who is creative, collaborative, and adaptable to join our agile team of data scientists, engineers, and UX developers. The role focuses on building and maintaining robust data pipelines to support advanced analytics, data science, and BI solutions.

As a Senior Data Engineer, you will work with internal and external data, collaborate with data scientists, and contribute to the design, development, and deployment of innovative solutions.

Key Responsibilities:

• Design, develop, test, and maintain optimal data pipeline and ETL architectures.
• Map out data systems and define/design required integrations, ETL, BI, and AI systems/processes.
• Prepare and optimize data for predictive and prescriptive modeling.
• Collaborate with teams to integrate ERP data into the enterprise data lake, ensuring seamless flow and quality.
• Enhance cloud data infrastructure on AWS or Azure for scalability and performance.
• Utilize big data tools and frameworks to optimize data acquisition and preparation.
• Build architectures to move data to/from data lakes and data warehouses for advanced analytics.
• Develop and curate data models for analytics, dashboards, and reports.
• Conduct code reviews, maintain production-level code, and implement testing approaches.
• Monitor, troubleshoot, and resolve data ingestion workflows to maintain reliability and uptime.
• Drive innovation and implement efficient new approaches to data engineering tasks.

Must-Have Skills:

• Bachelor’s degree in Computer Science, Mathematics, Engineering, or a related field.
• 5+ years of experience working with enterprise data platforms, including building and managing data lakes.
• 3–5 years of experience designing and implementing data warehouse solutions.
• Expertise in SQL, including developing stored procedures (SP) and applying advanced data design concepts.
• Proficiency in Spark (Python/Scala) and Spark Streaming for real-time data pipelines.
• Experience with AWS or Azure services (e.g., AWS Glue, Azure Data Factory, Redshift, Snowflake).
• Familiarity with big data tools such as Apache Kafka, Apache Spark, or Flink.
• Hands-on experience with orchestration tools (e.g., Apache Airflow, Prefect).
• Knowledge of CI/CD processes, version control (e.g., Git, Jenkins), and deployment automation.
• Strong problem-solving, communication, and collaboration skills.

Good-to-Have Skills:

• Experience in integrating ERP data into data lakes.
• Experience with traditional ETL tools (e.g., Talend, Pentaho).

Competencies:

• Tech Savvy - Anticipating and adopting innovations in business-building digital and technology applications.
• Self-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
• Action Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
• Customer Focus - Building strong customer relationships and delivering customer-centric solutions.
• Optimize Work Processes - Knowing the most effective and efficient processes to get things done, with a focus on continuous improvement.

Why Join Us?

• Be part of a collaborative and agile team driving cutting-edge AI and data engineering solutions.
• Work on impactful projects that make a difference across industries.
• Opportunities for professional growth and continuous learning.
• Competitive salary and benefits package.

Application Details

Ready to make an impact? Apply today and become part of the QX Impact team!

Job Title : Tech Lead – Data Lake Platform

Number of Positions : 2

Experience : 7+ Years

Role Type : Technical Lead / Data Platform Lead

Domain : Data Engineering / Data Platform / AWS

About the Role :

We are looking for an experienced Tech Lead – Data Lake Platform to lead the design, development, and operations of an enterprise-scale AWS-based Data Lake Platform.

The platform will ingest data from multiple business systems, process it through structured data layers, and serve data for analytics, reporting, APIs, and operational applications.

As a Tech Lead, you will be responsible for setting the technical direction, leading data engineering teams, driving platform reliability and performance, and owning the platform's delivery, governance, and production support end to end.

Core Tech Stack :

AWS | S3 | EMR | Glue | Athena | Redshift | DMS | Lambda | RDS | IAM | Airflow | Spark/PySpark | SQL | Data Modeling | Hasura | GraphQL | DBT | PostgreSQL/Aurora | Kafka

Key Responsibilities :

• Own the overall Data Lake Platform architecture, covering data ingestion, staging, curated layers, consumption, analytics, and API/data serving.
• Lead the design and development of production-grade data pipelines using AWS Glue, EMR/Spark, Airflow, DBT, Athena, and Redshift.
• Design scalable batch and analytics pipelines with a focus on reliability, performance, data quality, and maintainability.
• Own the API/data-serving architecture from consumption data → RDS/PostgreSQL → Hasura GraphQL → Lambda/API Gateway.
• Drive improvements in platform stability, including orchestration failures, cluster sizing, pipeline SLAs, query performance, and production reliability.
• Design and implement appropriate AWS security, access control, IAM, PII handling, and data governance practices.
• Lead technical discussions, architecture decisions, code reviews, and engineering best practices.
• Mentor and guide data engineers while ensuring high-quality and scalable engineering delivery.
• Own production support, incident management, troubleshooting, and root-cause analysis (RCA) for critical data platform issues.
• Develop and maintain runbooks, operational procedures, monitoring, and incident response practices.
• Collaborate with business, product, application, and analytics teams to onboard new datasets and support reporting, API, and data consumption requirements.
• Ensure the platform meets defined availability, performance, security, data quality, and compliance requirements.

Must-Have Skills :

• 7+ years of experience in Data Engineering, Data Platform Engineering, or related areas, including experience in technical leadership or architecture.
• Strong hands-on experience with AWS Data Services, including:
• Amazon S3
• AWS EMR
• AWS Glue
• Amazon Athena
• Amazon Redshift
• AWS DMS
• AWS Lambda
• Amazon RDS
• AWS IAM
• Strong production experience with Apache Airflow.
• Strong hands-on experience with Apache Spark / PySpark.
• Strong SQL skills and experience with data modeling, including layered data architecture, data marts, and enterprise data models.
• Experience with Hasura or a similar GraphQL layer for PostgreSQL-based data/API serving.
• Strong understanding of data lake architecture and enterprise data platforms.
• Proven experience leading engineers and driving technical decisions.
• Hands-on experience with production support, troubleshooting, incident management, and RCA.
• Strong understanding of data platform performance, scalability, reliability, and SLA management.

Good-to-Have Skills :

• Experience with DBT and modern data transformation practices.
• Experience with lakehouse table formats on Amazon S3, such as Apache Iceberg or similar technologies.
• Strong knowledge of Amazon Redshift workload optimization, including :
• Distribution keys
• Sort keys
• Spectrum
• External tables
• Experience with Kafka or other streaming/data ingestion technologies.
• Experience with PostgreSQL / Amazon Aurora as a data-serving layer.
• Experience with Lambda and API Gateway for API-based data serving.
• Experience with enterprise data governance, data quality, security, and compliance.
• Experience in BFSI / Banking / Financial Services / Insurance domain.

Ideal Candidate :

The ideal candidate is a hands-on Data Platform / Data Engineering Lead who can operate at both the architecture and implementation level. You should be comfortable designing an AWS Data Lake from end to end, leading engineers, troubleshooting production issues, and working closely with business and application teams to deliver reliable data products.

Job Description:

We are seeking a skilled Senior Data Engineer with expertise in Databricks to join our dynamic data team. The ideal candidate will design, build, and maintain scalable data pipelines and architectures to support our organization's data-driven initiatives and leverage Databricks to process large-scale datasets, optimize data workflows, enable advanced analytics and machine learning, and integrate Power BI for data visualization and reporting.

Key Responsibilities

·        5-8 years of professional work experience in a relevant field

·        Proficient in Microsoft Fabric platform, Azure Databricks, ADF, Delta Lake, SQL Data Warehouse, Unity Catalog.

·        Good Experience on Microsoft Dynamics 365

·        Experience/ prior knowledge on semi structure data and Structured Streaming, Azure synapse, data lake, data warehouse.

·        Proficient in creating Azure Data Factory pipelines for ETL/ELT processing; copy activity, custom Azure development etc.

·        Good knowledge of SQL and Python for data manipulation, transformation, and analysis

·        Understand business requirements to set functional specifications for reporting applications

• Data Pipeline Development: Design, develop, and maintain robust, scalable data pipelines using Databricks, Apache Spark, and other cloud-based technologies.
• Data Integration: Ingest, transform, and integrate data from diverse sources, including APIs, databases, streaming platforms, and third-party systems, into Databricks for analytics and reporting.
• Power BI Integration: Develop and optimize data models and datasets in Databricks for use in Power BI, ensuring efficient data connections and high-quality visualizations.
• Performance Optimization: Optimize data workflows, API calls, and queries on Databricks and Power BI for performance, cost-efficiency, and scalability.
• Data Modeling: Build and maintain data models to support business requirements, ensuring data quality, consistency, and accessibility for analytics and reporting.
• Cloud Integration: Implement data solutions on cloud platforms (e.g., AWS, Azure, GCP) integrated with Databricks, Power BI, and API ecosystems.
• Security & Compliance: Implement data governance, security, and compliance best practices within Databricks, Power BI, and API environments.
• Technical Skills:
• Proficiency in Databricks, including Delta Lake, Spark SQL.
• Strong programming skills in Python, Scala, or Java.
• Experience with Apache Spark for big data processing.
• Knowledge of SQL for querying and transforming data.
• Proficiency in Power BI for creating data models, DAX queries, and interactive dashboards.

Preferred Qualifications

• Databricks certification (e.g., Databricks Certified Data Engineer Associate/Professional).

Experience with real-time data processing and streaming