big data platform architect

Hexaware Technologies

Role Big Data Engineer Responsibilities Implement Data integration and Data Warehouse based solutions using Big Data Technologies. Should be highly proficient in the use of Big Data / Open Source Technologies and standard techniques of Data Integration; Data Manipulation with hands-on contribution. Should be able to develop cost efficient and performant data pipelines in the cloud platform In-depth understanding of modern big data technology; including Data modeling and machine learning skills Knowledge of real time data streaming and aggregation architectural patterns and practice Essential Skills: 3+ Years hands on knowledge on SQL as well as SQL/NoSQL databases Proficient in programming languages such as Python and PySpark/Scala/Java Experience with Apache Spark in On-prem / Cloud (Databricks / EMR / DataProc HDInsight) Working knowledge of API development Experience in building data pipelines in ETL/ELT methods Experience with integration of data from multiple data sources Experience in one of the NoSQL databases; such as HBase; Cassandra; MongoDB

Big Data Engineer

Brainlake

<div>
 The Data Platform Technology Architect leads the architecture, design, and evolution of the government's core data platforms &mdash; ensuring scalable, secure, and interoperable solutions. You will enable seamless data exchange and integration across government entities and oversee data ingestion, storage, APIs, master data management, and advanced analytics capabilities, all in alignment with Government-Wide strategy and policy.
 
 
 <ul>
 <li>
 Define and own the end-to-end architecture for core data platforms &mdash; ensuring scalability, performance, and security
 </li>
 <li>
 Advise on technology selection, solution architecture, and platform harmonization for optimal, future-proof, cost-effective deployments aligned to Government-Wide data strategy
 </li>
 <li>
 Lead architecture and oversight of a complex ecosystem of data platforms &mdash; ensuring interoperability, consistency, and alignment across multiple vendors and technologies
 </li>
 <li>
 Design and govern the data sharing platform, enabling secure, standardized data flows across entities
 </li>
 <li>
 Oversee implementation and evolution of platform components for sharing, provisioning, and performance measurement
 </li>
 <li>
 Guide design and implementation of entity-level data solutions: ingestion, storage (lakes/warehouses), catalogues, master data management, integration layers, and analytics
 </li>
 <li>
 Ensure interoperability across entities through standardized data exchange, APIs, and integration patterns
 </li>
 <li>
 Oversee platform lifecycle management &mdash; setup, operations, upgrades, migrations, and decommissioning of legacy platforms
 </li>
 <li>
 Engage stakeholders to align the platform roadmap with strategic priorities and evolving data use cases
 </li>
 <li>
 Collaborate with engineering teams and vendors to ensure high-quality platform delivery and deployment
 </li>
 <li>
 Drive platform adoption and value realization through enablement and use case activation
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 Bachelor's or Master's in Computer Science, Data Engineering, or related field
 </li>
 <li>
 20+ years in technology architecture &mdash; including designing and deploying large-scale data platforms in multinational or multi-entity environments (preferably with exposure in Europe or North America)
 </li>
 <li>
 Proven hands-on experience architecting and implementing cloud-based data platforms, data marketplaces, and integration ecosystems across entities
 </li>
 <li>
 Strong expertise across modern data platforms and technologies &mdash; e.g., Azure, Databricks, Snowflake, Informatica, Microsoft Purview, Palantir Foundry, or similar
 </li>
 <li>
 Demonstrated ability to assess, select, and harmonize technologies across diverse vendors
 </li>
 <li>
 Government or large-scale enterprise ecosystem experience strongly preferred
 </li>
 <li>
 Excellent English communication; Arabic a plus
 </li>
 </ul>
</div>

Data Platform Technology Architect

<div>
 
 Introduction
 
 
 
 A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You&rsquo;ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you&rsquo;ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You&rsquo;ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.
 
 
 
 Your Role And Responsibilities
 
 
 
 As a Data Engineer specializing in Data Platforms-Databricks, you will play a critical role in building and enhancing the Databricks platform for client's Data and AI use cases. This role involves implementing Data and AI use cases on the Databricks platform, requiring strong technical skills and attention to detail. Your primary responsibilities will include:
 
 
 <ul>
 <li>
 Implement Data and AI Use Cases: Implement data and AI use cases on the Databricks platform, ensuring seamless integration and optimal performance. This involves designing, developing, and deploying scalable data pipelines and architectures that meet business requirements.
 </li>
 <li>
 Enhance Databricks Platform: Continuously enhance and improve the Databricks platform to support evolving client needs and emerging trends in Data and AI. This includes staying up-to-date with new features, best practices, and industry developments related to Databricks and cloud-based data engineering.
 
 
 </li>
 </ul>
 
 Preferred Education
 
 
 
 Bachelor's Degree
 
 
 
 Required Technical And Professional Expertise
 
 
 
 <ul>
 <li>
 Exposure to Databricks Platform: Experience with implementing data and AI use cases on the Databricks platform, including designing, developing, and deploying scalable data pipelines and architectures.
 </li>
 <li>
 Data Engineering Skills: Experience working with data engineering principles, practices, and tools, particularly in cloud-based environments.
 </li>
 <li>
 Cloud Computing Knowledge: Exposure to cloud computing concepts, technologies, and platforms, including those related to data engineering and Databricks.
 </li>
 <li>
 Implementation Expertise: Experience with implementing data and AI solutions, ensuring seamless integration and optimal performance.
 </li>
 <li>
 Technical Skills: Proficiency in technical skills relevant to Databricks, data engineering, and cloud computing.
 
 
 </li>
 </ul>
 
 Preferred Technical And Professional Experience
 
 
 
 <ul>
 <li>
 Cloud Computing Knowledge: Exposure to cloud computing concepts, technologies, and platforms, including those related to data engineering and Databricks, is beneficial for this role.
 </li>
 <li>
 Data Engineering Principles: Familiarity with data engineering principles and practices, particularly in cloud-based environments, is advantageous for implementing data and AI use cases on the Databricks platform.
 </li>
 <li>
 Technical Proficiency: Proficiency in technical skills relevant to Databricks, data engineering, and cloud computing is desirable for this position.
 </li>
 </ul>
</div>

Data Engineer Data Platforms Databricks

<div>Title:
Big Data Systems Engineer
Belong, Connect, Grow, with KBR!
KBR&rsquo;s National Security Solutions (NSS) team provides high-end engineering and advanced technology solutions to our customers in the intelligence and national security communities. In this position, your work will have a profound impact on the country&rsquo;s most critical role &ndash; protecting our national security.
KBR is seeking a Big Data Systems Engineer to join our team. The successful candidate will be part of the KBR team supporting the Test Resource Management Center&rsquo;s (TRMC) Big Data (BD) and Knowledge Management (KM) Team deploying BD and KM systems for DoD testing Ranges and various acquisition programs.
Responsibilities:
<ul><li>The Big Data Systems Engineer will work on the deployment and integration of a highly visible data analytic project called Cloud Hybrid Edge-to-Enterprise Evaluation Test &amp; Analysis Suite (CHEETAS) at multiple DoD ranges and labs.</li><li>
As a Big Data Systems Engineer, you will be a critical part of our technical team responsible for deploying CHEETAS within customer environments. You will be the frontline interface that customers will have when first experiencing CHEETAS within their DoD Range and lab environments.</li><li>
This position will require you to work closely with system administrators and software developers to communicate, document and ultimately resolve deployment issues as they arise.</li><li>
You will deploy CHEETAS within disparate DoD testing Ranges and acquisition programs environments (on different non-standard hardware stacks and integrated into different existing ecosystems) sometimes located within DoD vaults with no outside internet connectivity.</li><li>
Work on the deployment and integration of a highly visible data analytic project called Cloud Hybrid Edge-to-Enterprise Evaluation Test &amp; Analysis Suite (CHEETAS) at multiple DoD ranges and labs</li><li>
Deploy CHEETAS within customer environments</li><li>
Work closely with system administrators and software developers to communicate, document and ultimately resolve deployment issues as they arise</li><li>
Deploy CHEETAS within disparate DoD testing Ranges and acquisition programs environments (on different non-standard hardware stacks and integrated into different existing ecosystems) sometimes located within DoD vaults with no outside internet connectivity</li></ul>
Work Environment:
<ul><li>Location: Remote - The candidate can either work in one of KBR&rsquo;s facilities or work from home, with a stable internet connection.</li><li>
Travel Requirements: This position is anticipated to require travel of 25% with surges possible up to 50% to support end users located at various DoD Ranges and Labs across the US (including Alaska and Hawaii).</li><li>
Working Hours: Standard</li></ul>
Basic Qualifications/Knowledge:<ul><li>
Must have an active TS/SCI Security Clearance to be considered for this position.</li><li>
This position requires a bachelor's degree in a STEM Computer Science, Data Science, Statistics or related, technical field, and 10 years of DoD experience. Entry level Big Data Engineers will NOT be considered due to the breadth of knowledge necessary to be successful in the position.</li><li>
Previous experience must include three (3) years of hands-on experience in the integration with and configuration of: SQL Server Big Data Cluster, CentOS, Ubuntu, RedHat, Windows Server, VMWare, etc.)</li><li>
Previous experience must include five (5) years of hands-on experience in big data environments.</li><li>
Must be adept at deploying and configuring Big Data and Knowledge Management tools in an enterprise environment.</li><li>
Must have extensive technical expertise in the configuration and troubleshooting of big data ecosystems.</li><li>
Must have excellent written and verbal communication skills and be comfortable assisting customers with installation and configuration of their big data infrastructure.</li><li>
Must have strong troubleshooting skills and the ability to become a CHEETAS deployment subject matter expert.</li><li>
Must be comfortable working with a wide range of stakeholders and functional teams at various levels of experience.</li><li>
Excellent interpersonal skills, oral and written communication skills, and strong personal motivation are necessary to succeed within this position.</li><li>
Experience with installation, configuration, integration with and usage of the following tools and technologies: NFS, SMB, S3, SQL Server, Windows Server, Windows 10/11, Linux (CentOS, Ubuntu, RedHat).</li><li>
Must be prepared to learn new business processes or CHEETAS application nuances every Agile sprint release (roughly every 6 weeks) prior to deploying to customer sites.</li><li>
Ability to problem solve, debug, and troubleshoot while under pressure and time constraints is required.</li><li>
Ability to communicate effectively about technical topics to both experts and non-experts at both the management and technical level is required.</li><li>
Ability to work independently and provide appropriate recommendations for optimal design, analysis, and development.</li><li>
Excellent verbal communications skills are required, as the Integration Engineer will be in frequent contact with the project technical lead, be taking direction from various government leads, and will frequently be interacting with end users to gather requirements and implement solutions while away from other team members.</li><li>
Excellent testing, debugging and problem-solving skills are required to be successful in this position.</li><li>
Experience designing, building, integrating with and maintaining both new and existing big data systems and solutions.</li><li>
Ability to speak and present findings in front of large groups.</li><li>
Ability to document and repeat procedures.</li><li>
This position is anticipated to require travel of 25% with surges possible up to 50% to support end users located at various DoD Ranges and Labs across the United States.</li></ul>
Preferred Qualifications:
<ul><li>Experience working in government/defense labs and their computing restrictions.</li><li>
Experience working with major DoD acquisition programs.</li><li>
Knowledge of the Test and Training Enabling Architecture (TENA), the Joint Mission Environment Testing Capability (JMETC) and distributed testing and training.</li><li>
Experience with working in distributed team environment.</li><li>
Ability to teach and mentor engineers with a variety of skill levels and backgrounds.</li><li>
Knowledge of DoD cybersecurity policies.</li></ul>
Basic Compensation:
$142,400 - $180,000 (For the Denver, CO Area only)
$148,900 - $200,000 (For the Los Angeles, CA Area Only)
$148,900 - $200,00 (For the Washington, DC Area Only)
The offered rate will be based on the selected candidate&rsquo;s working location, knowledge, skills, abilities and/or experience, clearance level, contract affordability and in consideration of internal parity.
Additional Compensation:
KBR may offer bonuses, commissions, or other forms of compensation to certain job titles or levels, per internal policy or contractual designation. Additional compensation may be in the form of a sign on bonus, relocation benefits, short-term incentives, long-term incentives, or discretionary payments for exceptional performance.
Belong, Connect and Grow at KBR 
 
At KBR, we are passionate about our people and our Zero Harm culture. These inform all that we do and are at the heart of our commitment to, and ongoing journey toward being a People First company. That commitment is central to our team of team&rsquo;s philosophy and fosters an environment where everyone can Belong, Connect and Grow. We Deliver &ndash; Together.
KBR is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity or expression, age, national origin, veteran status, genetic information, union status and/or beliefs, or any other characteristic protected by federal, state, or local law.</div>

Big Data Systems Engineer

HireOn

<div>
 
 Employment Type:
 
 Full-Time | Permanent
 
 
 
 Location:
 
 Pakistan (Lahore / Karachi / Islamabad) &ndash; Hybrid
 
 
 
 About The Opportunity
 
 
 
 
 HireOn
 
 is hiring on behalf of a
 
 reputed international client
 
 seeking a
 
 Principal Data Engineer
 
 to play a pivotal role in building and scaling a next-generation cloud data platform.
 
 
 This position focuses on designing and delivering
 
 high-performance data pipelines and warehouse solutions on Amazon Redshift
 
 , enabling faster analytics, improved reliability, and scalable data operations.
 
 
 You will take ownership of
 
 architecting data workflows, optimizing processing performance, and implementing production-grade orchestration
 
 , while collaborating with cross-functional teams to drive data-driven outcomes.
 
 
 
 Key Requirements (Must-Have First)
 
 
 
 <ul>
 <li>
 6 to 8+ years of experience in data engineering, data platforms, or data warehousing
 </li>
 <li>
 Advanced expertise in Amazon Redshift, including performance tuning, workload optimization, and MPP architecture
 </li>
 <li>
 Strong hands-on experience with Apache Airflow (MWAA preferred) for workflow orchestration
 </li>
 <li>
 Proficiency in SQL and Python for building scalable data pipelines
 </li>
 <li>
 Solid experience with cloud-based data ecosystems on AWS (S3, Lambda, CloudWatch, DMS)
 </li>
 <li>
 Deep understanding of data modeling, ETL/ELT frameworks, and large-scale data processing
 </li>
 <li>
 Proven ability to design high-performance, distributed data pipelines
 </li>
 <li>
 Experience leading engineering efforts, including code reviews, mentoring, and technical decision-making
 </li>
 <li>
 Strong knowledge of query optimization, parallel processing, and data pipeline efficiency
 
 
 </li>
 </ul>
 
 
 Core Technical Skills
 
 
 
 
 
 Essential
 
 
 
 <ul>
 <li>
 Amazon Redshift (Provisioned / Serverless)
 </li>
 <li>
 MPP data processing concepts
 </li>
 <li>
 Apache Airflow (MWAA)
 </li>
 <li>
 Advanced SQL &amp; performance tuning
 </li>
 <li>
 Python for data engineering
 </li>
 <li>
 Data modeling &amp; warehousing concepts
 
 
 </li>
 </ul>
 
 Preferred
 
 
 
 <ul>
 <li>
 AWS services (S3, Lambda, DMS, Aurora PostgreSQL)
 </li>
 <li>
 CI/CD pipelines &amp; Git-based workflows
 </li>
 <li>
 Linux/Unix environments
 </li>
 <li>
 Experience with large-scale, distributed data systems
 
 
 </li>
 </ul>
 
 What Success Looks Like
 
 
 
 <ul>
 <li>
 Highly optimized and scalable data pipelines running on Redshift
 </li>
 <li>
 Reliable orchestration with minimal downtime and strong observability
 </li>
 <li>
 Efficient handling of large datasets with improved query performance
 </li>
 <li>
 Well-architected data platform aligned with modern engineering standards
 
 
 </li>
 </ul>
 
 
 Why Join Through HireOn
 
 
 
 
 <ul>
 <li>
 Opportunity to work with a globally recognized client on high-impact data initiatives
 </li>
 <li>
 Exposure to modern cloud data technologies and large-scale systems
 </li>
 <li>
 Hybrid work flexibility within Pakistan
 </li>
 <li>
 Career growth into data architecture and platform leadership roles
 
 
 </li>
 </ul>
 Skills: apache airflow,python,plsql,aws,amazon redshift
</div>

Principal Data Engineer – AWS Redshift & Data Platforms

Fractal

<div>
 
 
 AI and Data Platform Engineer
 
 
 
 Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work&reg; Institute and recognized as a &lsquo;Cool Vendor&rsquo; and a &lsquo;Vendor to Watch&rsquo; by Gartner.
 
 
 Please visit
 
 Fractal | Intelligence for Imagination
 
 for more information about Fractal.
 
 
 
 
 We are looking for a strong AI Engineer who thrives at the intersection of AI assisted development, data platform engineering, and high velocity delivery.
 
 
 This role focuses on delivering high‑quality, scalable, and governed data assets through spec‑driven development, AI‑enabled engineering workflows, and standardized data patterns. You will work closely with platform, DevOps, SRE, and analytics consumers to enable self‑service analytics while reducing operational toil through automation and reuse.
 
 
 This is a hands‑on engineering role for someone who enjoys modern data engineering, high engineering rigor, and rapid, AI‑accelerated delivery. You may need to work with PST overlapping hours.
 
 
 
 
 
 
 
 Responsibilities:
 
 
 
 
 
 Spec‑Driven &amp; AI‑Accelerated Data Engineering
 
 
 <ul>
 <li>
 Deliver high‑quality data ingestion, transformation, and modeling solutions using Fabric and associated Azure services through spec‑driven development.
 </li>
 <li>
 Apply AI‑enabled development workflows using tools such as GitHub Copilot and LLM‑assisted coding to maximize development velocity without compromising quality.
 </li>
 <li>
 Translate business and analytics requirements into clear technical specifications, schemas, transformations, and acceptance criteria.
 </li>
 <li>
 Participate actively in automated reviews, AI‑assisted refactoring, and iterative improvements.
 </li>
 </ul>
 
 
 Data Pipelines &amp; Modeling
 
 
 <ul>
 <li>
 Build modular, reusable, and testable pipeline components aligned with standardized data patterns.
 </li>
 <li>
 Design and implement ingestion and transformation pipelines using Fabric‑native constructs (Lakehouse, Warehouse, Notebooks, Dataflows Gen2).
 </li>
 <li>
 Optimize transformations for scale, performance, and cost efficiency.
 </li>
 <li>
 Ensure pipelines are dependency‑aware, well‑orchestrated, and production‑ready.
 </li>
 </ul>
 
 
 
 
 
 Self‑Service Analytics Enablement
 
 
 <ul>
 <li>
 Produce consumer‑ready, tested, and governed datasets to enable self‑service analytics for business and analytics users.
 </li>
 <li>
 Apply strong data modeling practices (relational, dimensional, and analytical models) to support reporting and insights.
 </li>
 <li>
 Ensure data quality through validation checks, reconciliations, and automated testing.
 </li>
 <li>
 Platform Collaboration &amp; Production Readiness
 </li>
 <li>
 Collaborate closely with DevOps, SRE, and QA engineers to ensure production readiness and operational reliability.
 </li>
 <li>
 Follow consistent CI/CD, testing, and release practices across environments.
 </li>
 <li>
 Support investigation and resolution of data issues by improving pipeline robustness and observability.
 </li>
 </ul>
 
 
 Engineering Excellence &amp; Automation
 
 
 <ul>
 <li>
 Participate in code and design reviews, upholding engineering standards for quality, performance, and maintainability.
 </li>
 <li>
 Build reusable data assets and automated workflows to reduce manual data support and operational effort.
 </li>
 <li>
 Continuously improve reliability, reusability, and maintainability of data solutions.
 </li>
 </ul>
 
 
 
 Qualifications &amp; Experience
 
 : Core Requirements
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or master&rsquo;s degree in computer science, Engineering, or related field
 </li>
 <li>
 4+ years of professional software, data, or AI engineering experience
 </li>
 <li>
 Strong hands-on experience with:
 </li>
 <li>
 SQL (advanced querying and optimization)
 </li>
 <li>
 Data modeling (relational, dimensional, and analytical models)
 </li>
 <li>
 Distributed data processing concepts
 </li>
 <li>
 AI Engineering &amp; Development Velocity
 </li>
 <li>
 Demonstrated experience using AI assisted development / vibe coding to accelerate delivery
 </li>
 <li>
 Comfort working from detailed specifications rather than exploratory coding alone
 </li>
 <li>
 Strong engineering discipline: clean code, modular design, automated testing
 </li>
 <li>
 Fabric &amp; Azure (Required / Strongly Preferred)
 </li>
 </ul>
 
 
 Experience with strong exposure to:
 
 
 <ul>
 <li>
 Lakehouse, Warehouse, Notebooks, Dataflows Gen2
 </li>
 <li>
 OneLake concepts
 </li>
 </ul>
 
 
 Prior experience with:
 
 
 <ul>
 <li>
 Databricks (Spark, notebooks, pipelines)
 </li>
 <li>
 Azure services such as ADLS, Azure SQL, Synapse
 </li>
 <li>
 Ability to translate Databricks patterns into Fabric native implementations
 </li>
 </ul>
 
 
 
 Personal Attributes
 
 
 
 <ul>
 <li>
 Forward thinking, self-starter mindset
 </li>
 <li>
 Strong problem solving and systems thinking skills
 </li>
 <li>
 Comfortable operating in ambiguity with clear specifications as the stabilizing force
 </li>
 <li>
 Passion for building end to end, production grade AI and data platforms
 </li>
 </ul>
 
 
 
 
 
 Pay
 
 
 
 The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $110,000 - $120,000. In addition, for the current performance period, you may be eligible for a discretionary bonus.
 
 
 
 
 
 
 Benefits:
 
 
 
 As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides 11 paid holidays and 12 weeks of Parental Leave. We also follow a &ldquo;free time&rdquo; PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation.
 
 
 
 
 
 
 Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
 
 
 
 
</div>

AI Data Platform Engineer

Datamatics Global Services Ltd

<div><h1 class="jobSectionHeader">Job Description (JD)</h1><h3 class="jobSectionHeader">
Role: Data Architect / Tech Lead &ndash; Data Engineering &amp; Analytics</h3>
Location: Onsite - Riyadh 
Must Have: Experience over CVM implementation for Telco 
Experience: 8&ndash;12+ Years 
Industry Preference: Telecommunications<h2 class="jobSectionHeader">
About the Role</h2>
We are seeking a highly experienced Data Architect / Tech Lead to lead the design, development, and optimization of enterprise-scale data platforms. The ideal candidate will bring deep expertise in data architecture, ETL pipelines, and modern data ecosystems, along with strong leadership capabilities to drive large-scale transformation initiatives.<h2 class="jobSectionHeader">
Key Responsibilities</h2><ul><li>
Lead end-to-end data architecture design for enterprise data platforms and analytics solutions</li><li>
Design and implement scalable data warehouses, data lakes, and ETL pipelines</li><li>
Drive data modeling (logical &amp; physical) and semantic layer design</li><li>
Oversee data integration across multiple systems using tools like Informatica, DataStage, and Spark</li><li>
Lead large-scale initiatives such as system migrations, platform modernization, and architecture revamps</li><li>
Ensure data quality, governance, and compliance across data assets</li><li>
Build and optimize high-performance data pipelines and reporting systems</li><li>
Enable self-service BI and analytics using tools like Tableau, MicroStrategy, and QlikView</li><li>
Collaborate with business stakeholders to translate requirements into technical solutions</li><li>
Lead and mentor cross-functional teams including data engineers, analysts, and BI developers</li><li>
Drive automation in reporting and data operations</li><li>
Manage performance tuning and optimization of databases and queries</li></ul><h2 class="jobSectionHeader">
Required Skills &amp; Experience</h2><h3 class="jobSectionHeader">
Technical Skills</h3><ul><li>
Strong expertise in:<ul><li>
Teradata, Informatica, Apache Spark, Cloudera (Big Data ecosystem)</li><li>
ETL/ELT design and development</li><li>
SQL, Python, Shell Scripting</li></ul></li><li>
Hands-on experience with:<ul><li>
Data modeling tools (Erwin or equivalent)</li><li>
Data warehouse architecture</li><li>
Semantic layer design</li></ul></li><li>
Database experience:<ul><li>
Teradata, Oracle, MySQL, PostgreSQL</li></ul></li><li>
BI &amp; Reporting:<ul><li>
Tableau, MicroStrategy, QlikView</li></ul></li><li>
Familiarity with:<ul><li>
GitHub, C#, DataOps frameworks</li></ul></li></ul><h3 class="jobSectionHeader">
Functional Skills</h3><ul><li>
Data Architecture &amp; Solution Design</li><li>
Data Integration &amp; Pipeline Development</li><li>
Reporting &amp; Dashboard Automation</li><li>
Performance Optimization</li><li>
Data Governance &amp; Quality Assurance</li><li>
Business &amp; Data Analysis</li></ul><h2 class="jobSectionHeader">
Leadership &amp; Soft Skills</h2><ul><li>
Proven experience leading large-scale data programs</li><li>
Strong stakeholder management and communication skills</li><li>
Ability to manage cross-functional and distributed teams</li><li>
Experience in project delivery and technical governance</li></ul><h2 class="jobSectionHeader">
Education</h2><ul><li>
Bachelor&rsquo;s degree in Information Technology / Computer Science / Engineering or related field</li></ul><h2 class="jobSectionHeader">
Preferred Experience</h2><ul><li>
Telecom domain experience (highly preferred)</li><li>
Experience in large transformation programs (e.g., system revamps, migrations)</li><li>
Exposure to product ownership or project management roles</li></ul>
o79llUR4WA</div>

Tech Lead – Data Engineering & Analytics

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