• Integrate data from various sources into usable formats.
• Design and construct efficient data pipelines for analytics and reporting.
• Develop and maintain data quality components for infrastructure data.
• Automate data service deployments and infrastructure provisioning.
• Collaborate with stakeholders to understand and fulfill data requirements.

• Proficiency in Python and SQL for data manipulation.
• Experience with building and operating ETL pipelines.
• Strong understanding of data warehousing principles.

• Manage and configure Slack, Asana, and Intranet platforms.
• Provide advanced technical support for collaboration tools.
• Monitor system performance and optimize for efficiency.
• Independently manage daily operations and projects.
• Create and maintain technical documentation and training materials.

• Proven experience administering enterprise collaboration platforms.
• Ability to work autonomously and manage priorities.
• Strong technical and interpersonal communication skills.

• Design and deploy ETL/ELT data pipelines on GCP.
• Optimize data processing using Dataproc, BigQuery, and dbt.
• Develop and manage data models for analytics and AI.
• Ingest and transform data from various enterprise sources.
• Collaborate with stakeholders to define and implement data solutions.

• Experience with Google Cloud Platform
• Proficiency in SQL and BigQuery
• Experience with dbt for data transformation

Key Responsibilities:

• Develop and implement scalable data pipelines on Google Cloud Platform.
• Construct and refine enterprise data solutions using BigQuery and dbt.
• Design data models optimized for analytical and reporting needs.
• Create ETL/ELT processes to integrate diverse data sources.
• Lead technical design discussions and ensure code quality.

Must-Have Skills:

• Proven technical leadership in enterprise data projects.
• Deep expertise in Google Cloud Platform services.
• Proficiency in Dataproc, BigQuery, SQL, and dbt.

Domain / Industry Experience: Experience with retail apparel, supply chain platforms, transportation and logistics, or warehouse management systems is highly desirable.

ABOUT AKRAYA
Akraya is an award-winning IT staffing firm consistently recognized for our commitment to excellence and a thriving work environment. Most recently, we were recognized Stevie Employer of the Year 2025, SIA Best Staffing Firm to work for 2025, Inc 5000 Best Workspaces in US (2025 & 2024) and Glassdoor's Best Places to Work (2023 & 2022)!

Industry Leaders in Tech Staffing
As Talent solutions provider for Fortune 100 Organizations, Akraya's industry recognitions solidify our leadership position in the IT staffing space. We don't just connect you with great jobs, we connect you with a workplace that inspires!

Join Akraya Today!
Let us lead you to your dream career and experience the Akraya difference. Browse our open positions and join our team!

Key responsibilities:

• Optimize the Product Analytics (PA) model for improved stability, efficiency, and performance.
• Integrate and expand datasets to enhance global reporting and stakeholder impact.
• Automate data integrations to keep the PA model updated.
• Work closely with the Data Strategy team to identify and remedy pipeline issues.
• Develop and oversee data quality dashboards, ensuring data completeness and integrity.

Must-have skills:

• Hands-on experience with Anaplan platform management and optimization.
• Strong ability in handling large-scale data and automating processes.
• Proficiency in data quality monitoring and creating alerting workflows.

Industry Experience Required:

• Experience in Product Analytics or related field, with a proven track record of enhancing and maintaining data models to support product decisions and growth.

• Perform application and product security assessments for SaaS and on-prem DNS/DHCP solutions across microservices and containerized architectures
• Drive threat modeling for new and existing features, documenting abuse cases and prioritized mitigations
• Plan, execute, and analyze penetration tests, leveraging standard frameworks and AI-assisted tooling to accelerate test design and reporting
• Evaluate and tune SAST, DAST, SCA, and container security tools (e.g., Coverity, CodeQL, SonarQube, Contrast) to reduce noise and improve signal quality
• Partner with development teams to review designs and code changes, define secure coding patterns, and integrate security stories into agile backlogs
• Define and validate security controls for authentication, authorization, encryption, secrets management, and secure network communication
• Collaborate with cloud platform teams (AWS, GCP, Azure) to assess and harden Kubernetes, containers, and CI/CD pipelines, including policy-as-code
• Apply AI and automation tooling (e.g., code analysis assistants, LLM-assisted triage) to correlate findings, reduce false positives, and generate remediation guidance responsibly
• Lead security root-cause reviews for product issues and translate lessons learned into patterns, guardrails, and developer education
• Deliver targeted secure coding, OWASP Top 10/API, and threat modeling training for engineering and DevOps teams

• 5+ years of Application Security or Product Security experience
• Vulnerability Assessment (VA) & Penetration Testing (PT)
• Threat Modeling (STRIDE preferred)
• Static Application Security Testing (SAST)
• Dynamic Application Security Testing (DAST)
• Manual Web Application Penetration Testing
• Burp Suite
• OWASP ZAP (ZAP Proxy)
• Python OR Go OR Shell Scripting
• Security Automation
• Cloud Security – AWS OR Azure OR GCP
• ISO 27001 OR NIST
• Strong communication and stakeholder management skills

• Provide analytical support for annual sales planning including quota planning and go-to-market strategy.
• Convert raw data into meaningful business insights to drive decision-making.
• Establish and track performance standards and specific activities based on predefined success criteria for the sales organization.
• Monitor business performance metrics, identify areas for improvement, and recommend action steps.
• Use advanced Excel, Tableau, and SQL skills to report and analyze data effectively.

• Expert analytical abilities with a strong attention to detail.
• Advanced proficiency in Excel, Tableau, and SQL.
• Strong experience with Google's G Suite (Drive, Docs, Sheets, Slides).

• Design, develop, and maintain scalable ETL/ELT pipelines using AWS services including Glue, EMR, Lambda, Kinesis, Step Functions, Redshift, and S3.
• Architect and implement enterprise Data Mesh solutions with domain-oriented ownership, federated governance, and reusable self-service data product frameworks.
• Develop modern data platforms leveraging Databricks, Delta Lake, Unity Catalog, Starburst/Trino, Collibra, and Immuta.
• Implement enterprise data governance, metadata management, data lineage, access controls, and automated data quality monitoring frameworks.
• Build and maintain CI/CD pipelines, Infrastructure as Code (Terraform/CloudFormation), containerized deployments, and DevSecOps automation.
• Design and implement AI-powered solutions including RAG pipelines, agentic AI workflows, Amazon Bedrock, LLM integrations, and AI-assisted SDLC automation.
• Optimize cloud infrastructure for scalability, security, performance, and cost efficiency across hybrid cloud environments.
• Provide L3 production support, mentor engineering teams, and establish best practices for modern data engineering and Data Mesh adoption.
• Lead technical architecture reviews, collaborate with business stakeholders, and translate business requirements into scalable technical solutions.
• Create and maintain technical architecture documentation, implementation standards, and engineering best practices.

• 8+ years of experience in Data Engineering with at least 5+ years designing enterprise AWS Data Platforms.
• Strong hands-on experience with AWS S3, Glue, EMR, Redshift, Lambda, Kinesis, Lake Formation, and Step Functions.
• Proven experience implementing Data Mesh architecture, domain-driven data products, and federated governance.
• Expertise with Databricks (Delta Lake, Unity Catalog), Starburst/Trino, Collibra, and Immuta.
• Strong knowledge of ETL/ELT development, data modeling, metadata management, data lineage, and data quality frameworks.
• Experience with Terraform, CloudFormation, Docker, ECS, GitHub Actions/GitLab CI, Jenkins, and DevSecOps best practices.
• Experience implementing AI-enabled engineering solutions using Amazon Bedrock, LLMs, RAG architectures, Copilot, Claude Code, or similar AI frameworks.
• Strong SQL, Python, and cloud-native data engineering expertise.
• Experience supporting secure, compliant cloud environments, including AWS GovCloud or regulated environments.
• Excellent leadership, mentoring, architecture design, communication, and stakeholder management skills.

• Experience supporting enterprise data warehouse modernization initiatives.
• Knowledge of hybrid cloud and on-premises data platform integration.
• Experience implementing self-service analytics and reusable data product frameworks.
• Familiarity with cost optimization strategies for large-scale cloud data platforms.
• Experience working within highly regulated industries such as Financial Services or Government.

• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related technical field.
• AWS Certifications (Solutions Architect, Data Analytics, or Developer) preferred.
• Experience leading enterprise-scale cloud modernization and AI transformation initiatives.
• Strong background in enterprise architecture, technical leadership, and cross-functional collaboration.

• Design, develop, and maintain scalable ETL/ELT pipelines and enterprise data products on Google Cloud Platform (GCP).
• Build and optimize cloud-native data solutions using BigQuery, Dataproc, SQL, and dbt.
• Design robust dimensional, normalized, and analytical data models to support reporting, analytics, and AI use cases.
• Develop scalable data ingestion, transformation, validation, and publishing pipelines from multiple enterprise source systems.
• Collaborate with Product Managers, Business Analysts, Data Architects, and Solution Architects to translate business requirements into technical solutions.
• Lead technical design discussions, perform code reviews, and establish engineering best practices.
• Optimize data processing performance, scalability, reliability, and cloud cost efficiency.
• Implement monitoring, testing, CI/CD, and operational best practices for production data workloads.
• Develop reusable frameworks, engineering standards, and technical documentation.
• Troubleshoot production issues and drive continuous platform improvements.
• Mentor engineers and actively participate in Agile ceremonies, sprint planning, backlog refinement, and estimation.

• 8+ years of Data Engineering experience with technical leadership on enterprise data platforms.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Expert experience with BigQuery, Dataproc, SQL, and dbt.
• Strong experience designing and developing scalable ETL/ELT pipelines.
• Expertise in dimensional modeling, normalized data models, and cloud data warehouse architecture.
• Experience building cloud-native, scalable, and maintainable data pipelines.
• Experience with Git, CI/CD pipelines, and software engineering best practices.
• Excellent analytical, troubleshooting, and performance optimization skills.
• Strong communication skills with the ability to collaborate across technical and business teams.

• Apache Airflow workflow orchestration.
• Apache Kafka or other event streaming technologies.
• PySpark for distributed data processing.
• Python for data engineering automation and utilities.
• Data quality, metadata management, and data governance.
• Retail, Supply Chain, Transportation, Logistics, Warehouse Management Systems (WMS), or Distribution Center domain experience.

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
• Experience delivering cloud-native data platforms supporting enterprise analytics and AI initiatives.
• Proven leadership in mentoring engineers and driving engineering best practices.
• Experience working within Agile software development environments.
• Strong understanding of Supply Chain, Retail, Transportation, or Warehouse Management business processes.

• Design, develop, and maintain scalable ETL/ELT pipelines on Google Cloud Platform (GCP).
• Build and optimize cloud-native data solutions using BigQuery, Dataproc, SQL, and dbt.
• Design and maintain high-quality data models supporting enterprise reporting, analytics, and AI use cases.
• Ingest, transform, validate, and publish data from multiple enterprise source systems.
• Collaborate with Product Managers, Business Analysts, Data Architects, Solution Architects, and engineering teams to translate business requirements into scalable technical solutions.
• Develop reusable data transformation frameworks while adhering to engineering standards and best practices.
• Optimize data processing performance, scalability, reliability, and cloud cost efficiency.
• Perform unit testing, data validation, production support, and troubleshooting to ensure high-quality data delivery.
• Participate in Agile ceremonies including sprint planning, backlog refinement, code reviews, and technical discussions.
• Create and maintain technical documentation, data flow diagrams, and implementation artifacts.

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field (or equivalent practical experience).
• 4+ years of Data Engineering experience developing enterprise data solutions.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Expertise with BigQuery, Dataproc, SQL, and dbt (Data Build Tool).
• Strong understanding of ETL/ELT architecture and large-scale data processing.
• Experience with dimensional data modeling and analytical data warehouse concepts.
• Experience building scalable cloud-native data pipelines.
• Knowledge of Git, version control, and CI/CD best practices.
• Strong analytical, troubleshooting, problem-solving, and communication skills.
• Experience working in Agile development environments.

• Experience with Apache Airflow for workflow orchestration.
• Experience integrating with Apache Kafka or other event-streaming platforms.
• Working knowledge of PySpark for distributed data processing.
• Proficiency in Python for data engineering, automation, and scripting.
• Knowledge of data governance, metadata management, and data quality frameworks.
• Experience supporting analytics, AI, or machine learning data platforms.

• Experience working within Retail, Apparel, Supply Chain, Transportation, Logistics, Warehouse Management Systems (WMS), or Distribution Center Operations.
• Experience delivering enterprise cloud data solutions using modern GCP technologies.
• Strong understanding of scalable data architecture and cloud-native engineering best practices.
• Self-motivated team player with excellent collaboration skills and a passion for building reliable, high-performance data solutions.

• Design, develop, and implement scalable enterprise data pipelines and data products on Google Cloud Platform (GCP).
• Build and optimize cloud-native data solutions using BigQuery, Dataproc, SQL, and dbt.
• Develop scalable ETL/ELT pipelines to ingest, transform, and publish data from multiple enterprise systems.
• Design robust data models supporting enterprise reporting, analytics, and AI-driven decision-making.
• Collaborate with Product Managers, Business Analysts, Solution Architects, Data Architects, and business stakeholders to translate business requirements into technical solutions.
• Lead technical design discussions, architecture reviews, and code reviews while promoting engineering best practices.
• Optimize cloud data platforms for performance, scalability, reliability, and cost efficiency.
• Implement monitoring, testing, CI/CD, and operational best practices for production data pipelines.
• Develop reusable frameworks, engineering standards, and technical documentation to improve team productivity.
• Troubleshoot production issues, support continuous improvement initiatives, and mentor junior engineers.
• Participate in Agile ceremonies including sprint planning, backlog refinement, estimation, and technical planning.

• 8+ years of Data Engineering experience with demonstrated technical leadership on enterprise-scale projects.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Expert-level experience with BigQuery, Dataproc, SQL, and dbt.
• Strong knowledge of modern ETL/ELT architecture and large-scale cloud data processing.
• Expertise in data modeling, including dimensional modeling, normalized models, and analytical data warehouse design.
• Experience building scalable, maintainable cloud-native data pipelines.
• Strong experience with Git, CI/CD pipelines, and software engineering best practices.
• Excellent analytical, troubleshooting, and problem-solving skills.
• Strong communication and collaboration skills with cross-functional technical and business teams.

• Experience with Apache Airflow for workflow orchestration.
• Experience integrating enterprise data platforms using Apache Kafka or other streaming technologies.
• Working knowledge of PySpark for distributed data processing.
• Proficiency in Python for automation, utilities, and data engineering.
• Experience implementing data quality frameworks, metadata management, and data governance best practices.
• Experience supporting AI/ML data platforms and enterprise analytics initiatives.

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or a related field.
• Experience within Retail, Apparel, Supply Chain, Transportation, Logistics, Warehouse Management Systems (WMS), or Distribution Center Operations.
• Proven experience leading technical teams and mentoring engineers in Agile environments.
• Strong understanding of enterprise data architecture, cloud-native engineering, and modern analytics platforms.
• Passion for building scalable, reusable, and high-performance data solutions that enable enterprise analytics and AI capabilities.

• Analyze system dependencies for data disposition.
• Evaluate integration points and data removal impacts.
• Collaborate with stakeholders on personal information handling.
• Trace data flows using lineage tools to confirm business rules.
• Ensure data disposition aligns with retention and regulatory needs.

• Proficiency in SQL.
• Experience with data lineage tools.
• Understanding of data governance and regulatory compliance.