Data Architect (Dremio Lakehouse

Strong Databricks Architect Profile with end-to-end Lakehouse ownership

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Mandatory (Experience 1) – Must have 10+ years of software engineering experience with atleast 5+ years in Data Engineering with hands on exposure to Databricks and strong ownership of end-to-end data pipeline development.

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Mandatory (Experience 2) – Must have atleast 5+ years of expertise across the Databricks ecosystem — Delta Lake, Delta Live Tables, Autoloader, Structured Streaming, Workflows, Unity Catalog

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Mandatory (Tech skill 1) – Must have worked at architecture level, owning end-to-end design through deployment

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Mandatory (Tech skill 2) – Must have strong experience with Python and SQL for data processing and Apache Spark for performance tuning & scalability

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Mandatory (Tech skill 3) – Must have experience in large-scale data warehousing & advanced data modeling (3NF and dimensional) across batch and real-time systems

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Mandatory (AI Exposure) – Must have at least a basic working understanding of how AI services or tools work

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Mandatory (Communication) – Must have strong stakeholder management & requirement-gathering experience with US or UK clients

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Mandatory (Company) – Must come from a B2B IT services or IT consulting background

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Mandatory (Note) – CTC is inclusive of 5% variable

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Preferred (Tech skill 1) – Azure Databricks or Azure data services experience (project runs on Azure DevOps)

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Preferred (Tech skill 2) – MLflow or MLOps practices and AI use cases (RAG, AI/BI)

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Preferred (Tech skill 3) – CI/CD, Databricks Asset Bundles (DABs) or equivalent packaging, Terraform or IaC, reusable deployment templates

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Preferred (Integrations) – ServiceNow or enterprise system integrations

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Preferred (Certifications) – Databricks (Data Engineer Associate or Professional, ML or GenAI tracks), Azure or AWS cloud certifications

Job description: Data Architect – Databricks / AWS

Job Summary

We are looking for an experienced Data Architect to define and drive the target data architecture for the Horizon MVP and its future evolution. The role will be responsible for designing a scalable, secure, governed cloud data platform covering ingestion, storage, processing, analytics, APIs, and downstream data consumption.

The architect will work closely with Data Engineering, Backend, DevOps, QA, and business stakeholders to establish architecture standards and ensure the platform is ready for advanced analytics, AI/ML, vector storage, and future LLM-based capabilities.

• Job Title: Data Architect
• Experience: 8+ Years
• Relevant Architecture Experience: 3+ Years in Data Architecture
• Location: Chennai / Pune
• Work Mode: Hybrid – 3 Days WFO
• Budget: Up to 24 LPA
• Payroll: Haparz
• Notice Period: Immediate Preferred

Key Responsibilities

• Define the target data architecture for the Horizon MVP and establish an architecture roadmap for future scalability.
• Design end-to-end architecture covering data ingestion, storage, processing, serving, reporting, APIs, and downstream applications.
• Establish canonical data models and schemas for travel signals, corridors, sources, evidence, scores, and generated insights.
• Define data normalization strategies for structured, semi-structured, and unstructured data from multiple external sources.
• Design and govern the Databricks platform architecture, including Unity Catalog, data schemas, access controls, and governance standards.
• Establish data-retention, lineage, data-quality, security, privacy, and compliance controls.
• Define secure integration patterns between Databricks, AWS PRODIGY, SharePoint, external APIs, and downstream applications.
• Design scalable data processing for 30-day signal windows, convergence/divergence scoring, corridor ranking, and spike detection.
• Define architecture patterns that support future vector storage, embeddings, LLM integration, and multi-year analytics.
• Design reliable batch and API-driven ingestion frameworks for structured and unstructured data.
• Review technical designs, identify architectural risks, and provide technical direction to engineering teams.
• Guide backend, data engineering, DevOps, and QA teams in implementing architecture standards.
• Ensure architecture decisions align with enterprise security, RBAC, PII handling, privacy, and operational requirements.
• Communicate architecture decisions, trade-offs, and technical recommendations effectively to technical and business stakeholders.
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What We’re Looking For

• 8+ years of experience in data engineering, data platforms, or data architecture, with at least 3+ years in a Data Architect capacity.
• Strong hands-on experience designing cloud-based data platforms, lakehouses, or analytical platforms.
• Advanced knowledge of Databricks, Apache Spark/PySpark, Delta Lake, and Unity Catalog.
• Strong understanding of AWS data services, IAM, networking, and secure cloud integration patterns.
• Strong expertise in data modelling, metadata management, data lineage, data quality, retention, and governance.
• Experience architecting batch and API-based ingestion pipelines for structured, semi-structured, and unstructured data.
• Understanding of AI/ML workloads, feature pipelines, vector databases, embeddings, and LLM integration patterns.
• Experience designing APIs and downstream data-serving architectures.
• Strong knowledge of PII protection, RBAC, data privacy, and enterprise security controls.
• Excellent architectural communication and stakeholder-management skills.

10+ years of experience in Data Architecture, Data Engineering, or Data Platforms

• Strong expertise in IBM DB2 / On-Prem Relational Databases

• Strong expertise in PostgreSQL

• Hands-on experience with the Azure Data Ecosystem, including:

▪ Azure Data Factory (ADF)

▪ Azure Data Lake Storage Gen2 (ADLS Gen2)

▪ Azure Databricks / Synapse Analytics

▪ Azure Event Hub / Service Bus

▪ Azure Functions

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!

About the Role

You'll be at the forefront of designing and implementing robust data platform solutions that power advanced analytics, AI, and machine learning. Working with modern cloud technologies, you'll build scalable data foundations that enable clients to make smarter, data-driven decisions.

Key Responsibilities

• Build scalable data pipelines using Snowflake, AWS, GCP, and Databricks.
• Design and optimize data models for AI and machine learning workloads.
• Develop reliable data foundations for MLOps, governance, and data lineage.
• Integrate data from multiple sources into modern data platforms.
• Leverage Snowpark ML and Snowflake's native AI capabilities.
• Ensure data platforms are secure, scalable, and high-performing.

What We're Looking For

• 5+ years of hands-on experience with Snowflake.
• Strong proficiency in SQL and Python.
• Experience with AWS, Azure, or GCP.
• Knowledge of cloud storage services such as S3, ADLS, or GCS.
• Strong understanding of Dimensional Modeling and Data Vault.
• Experience with Scala or Java is a plus.

Tech Stack

• Data Warehouse: Snowflake
• Programming: SQL, Python, Scala (Good to Have), Java (Good to Have)
• Cloud: AWS, Azure, GCP
• Storage: S3, ADLS, GCS
• AI/ML: Snowpark ML, MLOps

Perks & Benefits

• Public Speaking & Communication Program
• Mentoring Program with Senior Support Leads
• 360° Progress Reviews
• Weekly Learning Sessions & Guilds
• Paid Certifications
• Hackathons & Innovation Days
• Recognition & Rewards Programs
• Team Socials & Annual Offsites
• Employee Assistance Program (24/7 Wellbeing Support)

The Data People Shaping Tomorrow

Our client helps organizations unlock the power of data through modern cloud, analytics, and AI solutions. We believe in creating an environment where talented technologists can learn, innovate, and make a real impact while building cutting-edge data platforms for global clients. If you're passionate about data engineering and want to work with the latest technologies in AI, cloud, and analytics, we'd love to hear from you.

Data Engineer

Data Lakehouse & Platform Engineering 

About the Role

We are hiring Data Engineer to own the lifecycle of our enterprise Data Lakehouse platform. We are looking for engineers who think in systems, make platform-level design decisions, and can build and operate a production-grade, multi-source lakehouse from the ground up, covering ingestion through consumption across a complex, multi-cloud source landscape.

You will be the technical authority for a platform that consolidates data from 18+ enterprise products (Costpoint, GovWin, Specpoint, Vantagepoint, and others) into a governed, medallion-architected data lake on AWS S3 with Apache Iceberg table format, orchestrated via AWS Step Functions, and queryable through AWS Athena and Trino. This role is end-to-end: you own ingestion, transformation, quality, orchestration, ML data supply, and BI consumption.

Key Responsibilities

•      Architect and evolve the full medallion lakehouse — Bronze, Silver, and Gold layers — on AWS S3 with Apache Iceberg; own schema design, partitioning, compaction, and retention policies.

•      Design and implement scalable Glue ETL (PySpark) pipelines for bronze_to_silver and silver_to_gold transformations, incorporating dbt for SQL-layer transformations where appropriate.

•      Own and extend CDC ingestion via Fivetran; manage schema evolution, connector health, and sync reliability across 18+ source products.

•      Build and maintain AWS Step Functions state machines and EventBridge schedules for end-to-end pipeline orchestration; implement Lambda-based quality and drift monitors.

•      Govern the Glue Catalog and Lake Formation policies; enforce column-level security, row-level access controls, and audit logging to meet SOC2 and regulatory requirements.

•      Architect the query layer — optimize Athena workgroups and partition pruning; plan and execute Trino-on-EKS deployment for sub-second analytics workloads.

•      Partner with data science teams on SageMaker data supply: feature engineering pipelines, training dataset preparation, and model registry integration.

•      Implement real-time and near-real-time streaming solutions using Kafka or Kinesis where sub-13-minute latency is required.

•      Lead platform modernization initiatives: evaluate emerging formats (Iceberg vs. Delta Lake vs. Hudi), tooling, and cost optimization strategies.

•      Establish and enforce data engineering best practices: code reviews, CI/CD for pipeline code, IaC (Terraform / CloudFormation), and incident response runbooks.

•      Mentor and level up junior and mid-level data engineers; define team standards for pipeline design, testing, and documentation.

Required Qualifications

•      Software or data engineering experience, with at least 4 years in an architect or technical lead capacity designing large-scale cloud data platforms.

•      Deep, hands-on expertise with AWS data services: S3, Glue (PySpark ETL), Athena, Step Functions, Lambda, EventBridge, Lake Formation, SageMaker, and CloudWatch.

•      Production experience with Apache Iceberg (or Delta Lake / Hudi) table formats — compaction, snapshot management, schema evolution, and time travel.

•      Strong PySpark and Python skills; ability to write, review, and optimize distributed data processing jobs at scale.

•      Hands-on experience with CDC-based ingestion platforms (Fivetran, Debezium, or equivalent) across heterogeneous source systems.

•      Proven experience designing and implementing medallion (Bronze/Silver/Gold) or equivalent multi-hop lakehouse architectures.

•      Experience with data pipeline orchestration: AWS Step Functions, Apache Airflow, or equivalent; event-driven pipeline design patterns.

•      Strong SQL skills; experience with Athena, Trino, Presto, or equivalent query engines for large-scale analytical workloads.

•      Familiarity with data governance tooling: catalog management (Glue Catalog, Apache Polaris/Iceberg REST), data lineage, access controls, and audit frameworks.

•      Experience with Infrastructure as Code (Terraform or CloudFormation) for data platform provisioning and drift management.

•      Solid understanding of dimensional modeling, schema design (star/snowflake), and data normalization for BI and analytics workloads.

•      Bachelor's degree in Computer Science, Engineering, or a related field; or equivalent professional experience.

Preferred Qualifications

•      Experience operating Trino or PrestoDB on Kubernetes (EKS); tuning for sub-second query latency and multi-tenant workloads.

•      Familiarity with streaming platforms (Kafka, Kinesis, or Pub/Sub) and real-time lakehouse patterns.

•      Experience with Apache Polaris or other Iceberg REST catalog implementations.

•      Exposure to SageMaker MLOps pipelines, Model Registry, and feature store patterns for ML data supply.

•      Experience with dbt (data build tool) for SQL-layer transformation and documentation in lakehouse environments.

•      Government contracting or ERP domain knowledge (Costpoint, Deltek, Oracle, or similar enterprise platforms) is a strong plus.

•      AWS certifications: Data Engineer Associate, Solutions Architect Professional, or equivalent.

What You Will Build

You will be a founding architect of a strategic, cross-product data platform that serves 18+ enterprise applications and their analytics, ML, and AI workloads. The decisions you make on schema, storage format, query layer, governance, and orchestration will shape the data foundation of the company for years. This is a high-impact, high-ownership role with direct visibility to senior leadership.

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.
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Experience

8+ Years

Azure Data Factory and Azure Databricks, processing 5 million+ records/week from 4+ source systems into a governed Lakehouse. 

Job Description:

Position: Lead / Senior Data Engineer

Location: Chennai

Shift: US Eastern Time ( 5:00 PM – 2:00 AM )

Experience : 8+ years

Notice Period: Immediate Joiner only

Roles and Responsibilities:

Role Overview

The Lead Data Engineer will be responsible for designing, developing, and delivering high-quality software and data solutions while leading a team of engineers. The role involves hands-on technical work, architectural decision-making, mentoring junior developers, and collaborating with cross-functional teams to ensure successful delivery of scalable data platforms and analytical solutions.

Key Responsibilities:

Lead the end-to-end design, development, and delivery of software systems, data pipelines, and components.

Define technical strategy, architecture, and best practices for development and data engineering.

Design and build optimized data pipelines using cutting-edge technologies in a cloud environment.

Construct infrastructure for efficient ETL processes from various sources and storage systems.

Architect, design, and maintain database pipeline architectures, ensuring readiness for AI/ML transformations.

Lead the implementation of algorithms and prototypes to transform raw data into useful information.

Review code for quality, scalability, and performance.

Collaborate with Product Managers, Business Managers, Designers, and QA teams to translate business requirements into technical solutions.

Develop analytical tools, programs, and reporting mechanisms.

Create data validation methods and data analysis tools.

Interpret data trends and patterns to establish operational alerts.

Conduct complex data analysis and present results effectively.

Prepare data for prescriptive and predictive modeling.

Ensure compliance with data governance and security policies.

Troubleshoot, debug, and resolve complex technical issues.

Drive continuous improvement in software and data development processes, tools, and methodologies.

Mentor and guide engineers through code reviews, technical discussions, and training.

Ensure timely delivery of projects while maintaining high engineering standards.

Continuously explore opportunities to enhance data quality and reliability.

Apply strong programming and problem-solving skills to develop scalable solutions.

Demonstrate passion for testing strategy, problem-solving, and continuous learning.

Willingness to acquire new skills and knowledge.

Possess a product/engineering mindset to drive impactful data solutions.

Experience working in distributed environments with global teams.

Stay current with emerging technologies and industry trends to propose innovative solutions.

Technical Skills and Experience Requirements

Minimum 8+ years of hands-on experience designing, building, deploying, testing, maintaining, monitoring, and owning scalable, resilient, and distributed data pipelines.

High proficiency in Python, Scala, and Spark for applied large-scale data processing.

Expertise with big data technologies, including Spark, Data Lake, Delta Lake, and Hive.

Solid understanding of batch and streaming data processing techniques.

Proficient knowledge of the Data Lifecycle Management process, including data collection, access, use, storage, transfer, and deletion.

Expert-level ability to write complex, optimized SQL queries across extensive data volumes.

Experience with RDBMS and OLAP databases such as MySQL and Snowflake.

Familiarity with Agile methodologies.

Obsession for service observability, instrumentation, monitoring, and alerting.

Knowledge or experience in architectural best practices for building data lakes.

Qualifications - Bachelor’s degree in computer science, Engineering, Information Systems, or related field.

• Design, build, and maintain scalable ETL/ELT pipelines for batch and real-time data ingestion and transformation.
• Develop and optimize data lake and data warehouse architectures (e.g., Snowflake, BigQuery, Redshift).
• Work with cloud platforms GCP, Azure to manage data infrastructure.
• GCP as mandatory skills
• Collaborate with analytics and product teams to understand data needs and deliver solutions.
• Ensure data quality, reliability, security, and compliance across all data systems.
• Mentor junior data engineers and contribute to best practices and code reviews.
• Monitor and troubleshoot data pipeline performance and resolve data-related issues.
• Automate data validation, monitoring, and alerting processes.
• 8+ years of experience in data engineering or software engineering with a data focus.
• Proficient in SQL and at least one programming language (e.g., Python, Scala, Java).
• Experience with modern data warehousing tools (e.g., Snowflake, Redshift, BigQuery).
• Strong understanding of data modeling, data lakes, and ETL/ELT design.
• Hands-on experience with orchestration tools like Airflow, dbt, or similar.
• Solid experience with cloud data platforms (AWS/GCP/Azure).
• Familiarity with CI/CD pipelines, containerization (Docker/Kubernetes), and version control (Git).
• Experience working in a DevOps or DataOps environment.
• Knowledge of data governance, lineage, and cataloging tools (e.g., Collibra, Alation).
• Familiarity with streaming technologies (Kafka, Spark Streaming, Flink).
• Experience supporting machine learning workflows and data science initiatives.