Senior Data Engineer (AWS Databricks) at Blend360

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

21 May, 26

Salary

0.0

Posted On

20 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Databricks, AWS, Python, Data Pipelines, Orchestration, Ingestion, CI/CD, GitHub, Monitoring, Logging, Alerting, Security, Governance, AWS Services, Workflow Management

Industry

Professional Services

Description

Company Description Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com We are seeking a AI Engineer to contribute to our next level of growth and expansion. Job Description We are seeking a Data Engineer to join our team, focused on data platform integration and pipeline engineering on Databricks. This role will work closely with AI Engineers to enable model development and deployment by building secure, reliable, and scalable data pipelines and integrations. The role is not focused on data transformation or analytics modelling. Instead, it concentrates on ingestion, orchestration, connectivity, and operational pipelines that support AI and advanced analytics workloads within Databricks environments deployed into client-managed accounts. Experience Required: 5+ yrs Key Responsibilities Databricks-Centric Data Engineering Build and maintain data pipelines that ingest data into Databricks on AWS. Configure and manage Databricks jobs and workflows to support AI workloads. Integrate Databricks with upstream source systems and downstream AI services. Ensure data is accessible and performant for AI training and inference use cases. Pipeline Engineering (Non-Transformational) Design and implement pipelines focused on: Data ingestion Data movement Orchestration and scheduling Develop pipelines using Python and Databricks-native tooling (e.g. Databricks Jobs, workflows). Ensure pipelines are production-ready with monitoring, logging, and alerting. AWS Environment Integration Work within client-owned AWS environments, collaborating with DevOps engineers on infrastructure provisioning. Integrate Databricks pipelines with cloud services such as: Ensure pipelines align with client security and governance requirements. Security, Governance & Compliance Build and operate pipelines that meet SOC 1 compliance requirements, including: Access controls and permissions Audit logging and traceability Controlled deployment processes Support data governance standards within Databricks environments. Delivery & Operations Deploy pipeline code via GitHub-based CI/CD pipelines. Support operational monitoring and incident response for data pipelines. Document pipeline designs, dependencies, and operational processes. Qualifications Core Technical Skills Strong experience as a Data Engineer, with a focus on Databricks-based platforms. Hands-on experience with Databricks on AWS, including: Jobs and workflows Cluster configuration (user-level understanding) Proficiency in Python for pipeline development. Experience using GitHub and CI/CD workflows. Engineering & Delivery Experience building production-grade ingestion and orchestration pipelines. Ability to work effectively in client-facing delivery environments. Strong documentation and collaboration skills. Nice to Have Experience with Databricks Unity Catalog. Exposure to event-driven or streaming data architectures. Familiarity with MLOps concepts and AI lifecycle support.

Responsibilities

The role focuses on building and maintaining data pipelines within Databricks on AWS, concentrating on ingestion, orchestration, and connectivity to support AI workloads. Key tasks include integrating Databricks with source systems and downstream AI services while ensuring pipelines are production-ready with monitoring and logging.