Blend360

Data Engineer

• Design and implementation of scalable, domain-oriented data solutions for a high-impact enterprise initiative.
• Work within a specific data domain (Ingestion, Customer Data, Enrichment, or Marketing Signals).
• Ensure high-quality, reliable data pipelines that power analytics and business-critical workflows.
• Build and optimize batch and streaming ingestion pipelines, including CDC-based architectures.
• Support the design and implementation of data models aligned with business and analytics needs.
• Ensure data quality, reliability, and performance across pipelines and datasets.
• Collaborate with cross-functional teams to align technical solutions with business requirements.
• Apply best practices in data engineering, including modular design, reusability, and governance.
• Leverage AI-assisted development tools to improve productivity and code quality.
• Proactively identify risks, bottlenecks, and optimization opportunities across data workflows.

Senior Data Engineer

• Build, maintain, and optimize scalable data solutions to support Journey Analytics initiatives.
• Maintain, optimize, and automate existing code repositories in GitHub.
• Refactor legacy code to simplify maintenance, updates, and reuse across multiple use cases.
• Design and build modular, reusable code components to support multiple journeys and reduce duplication.
• Develop and manage automated data pipelines in Databricks to support Journey Analytics datasets and downstream reporting.
• Consolidate key KPIs, metrics, and attributes into standardized data structures to enable flexible journey views.
• Build and maintain scalable data models to support current and future journey analytics use cases.
• Ensure data quality, performance, and reliability across data pipelines and analytics datasets.
• Collaborate with analytics and engineering teams to improve data processes and architecture.

• Build and scale data pipelines and supporting infrastructure on AWS
• Develop and implement data transformations, including: Design and configure AWS Glue jobs, Spark transformations, and Python-based ETL pipelines
• Write and maintain integration tests to validate data movement across raw → staged zones
• Implement data quality frameworks, including: Ensure data reliability, consistency, and performance across pipelines
• Collaborate with cross-functional teams to improve data architecture and processes
• Leverage AI-assisted development tools to generate boilerplate code (60–70%) and accelerate development cycles

DevOps Engineer

• Design and implement AWS cloud infrastructure and deployment patterns for the data platform, including multi-account AWS Organizations strategy, IAM design, networking, naming conventions, and tagging standards.
• Build and maintain CI/CD pipelines to support repeatable, controlled releases across Development, Test, and Production environments.
• Provision and configure AWS infrastructure as code (Terraform), including services such as AWS Glue, Amazon S3, Amazon Redshift, VPC networking, VPN/Direct Connect connectivity, Route 53, security groups, and firewall controls to connect on-premises source systems.
• Configure Git-based integration and deployment workflows for platforms such as Databricks or Snowflake to enforce version-controlled deployments.
• Support deployment of backend services, orchestration components, data services, APIs, and front-end applications.
• Enable monitoring, logging, alerting, and telemetry using services such as Amazon CloudWatch, AWS CloudTrail, AWS Config, and observability platforms like Datadog.
• Define and implement operational controls for reliability, performance, scalability, backup/recovery, and incident response.
• Implement and enforce secure access patterns using AWS IAM, IAM Identity Center (AWS SSO), AWS Secrets Manager, AWS KMS, and policy-driven access controls, including row-level and column-level security requirements where applicable.
• Ensure the solution aligns with architecture, security, governance, and service transition requirements.
• Support non-functional testing, release readiness, and path-to-production activities.
• Produce comprehensive operational runbooks, platform documentation, and a full IaC handover package enabling the client’s internal IT team to take ownership of platform operations at programme close.
• Support cost management, network performance tuning, and security hardening of the AWS platform; contribute to FinOps reporting and disaster recovery planning.

Lead Cloud – DevOps Engineer

Lead Cloud, DevOps Engineer

Solutions Engineer

• Lead solution architecture on consulting engagements for Intelligent Applications
• Structure interviews/workshops; synthesize current-state architecture and integration reality
• Own the architecture narrative with client executives
• Own architecture engagement through the engagement lifecycle

Director of Solutions Architecture

AI Engineer

• Collaborate with cross-functional teams to design and build AI-powered applications and internal platforms.
• Contribute to backend services, APIs, integrations, and frontend components when needed.
• Support the development and deployment of scalable AI and LLM-based solutions.
• Translate business and technical requirements into functional solutions.
• Rapidly prototype, test, and iterate on new ideas and AI use cases.
• Work in fast-changing environments with evolving priorities and technologies.
• Contribute to reusable engineering patterns, internal tooling, and shared components.
• Participate in code reviews, technical discussions, and collaborative problem-solving.
• Learn and adopt new frameworks, cloud technologies, and AI tools as required by the project.
• Maintain high standards for code quality, collaboration, and continuous improvement.

Fullstack AI Engineer

• Design, develop, and deploy scalable AI-powered applications and services.
• Translate ambiguous business problems into robust technical solutions.
• Build and maintain backend systems, APIs, integrations, and AI workflows.
• Collaborate across frontend, backend, cloud, and AI initiatives to deliver end-to-end solutions.
• Rapidly prototype and iterate on AI-driven products and internal accelerators.
• Contribute to architecture decisions, engineering standards, and reusable system patterns.
• Mentor junior engineers and support technical growth across the team.
• Drive improvements in scalability, reliability, performance, and developer experience.
• Work closely with stakeholders to balance business needs, technical trade-offs, and delivery timelines.
• Continuously evaluate and adopt emerging AI tools, frameworks, and engineering practices.

Senior AI Engineer

Analytics Engineer

• Implement the curated data model layer in dbt across 18 data domains, working closely with the Onshore Analytics Engineering Lead.
• Design and build dbt models across the full layered architecture: staging, intermediate, and mart layers.
• Implement comprehensive dbt testing strategies, including unique, not_null, accepted_values, relationships, and custom tests to ensure data quality and reliability.
• Develop and optimize incremental models to handle high-volume domains processing billions of events efficiently.
• Generate and maintain thorough project documentation, including column descriptions and lineage graphs, to support transparency and team collaboration.

QA Engineer

• Validate data accuracy and ensure production readiness by working closely with the Data Engineering Manager and Analytics Engineering Lead across 18 data domains.
• Design and build a reconciliation framework to systematically compare legacy pipeline outputs against new pipeline outputs, identifying discrepancies and gaps.
• Execute structured acceptance testing for each pipeline prior to promotion to production environments.
• Validate identity resolution accuracy through rigorous analysis of match rates, false positives, and false negatives.
• Document end-to-end data lineage across all 18 domains to support auditability, transparency, and regulatory compliance.
• Build and maintain automated regression test suites to enable continuous quality assurance as pipelines evolve.

Blend360

Senior Data Engineer

Senior Data Engineer

Data Engineer

Lead Cloud – DevOps Engineer

Lead Cloud, DevOps Engineer

Director of Solutions Architecture

Fullstack AI Engineer

Senior AI Engineer

Analytics Engineer

Data QA Analyst

Analytics Engineer

Data QA Analyst

Analytics Engineer

Data QA Analyst

Data QA Analyst