Key Responsibilities

Design, build, and maintain CI/CD pipelines using Azure Dev Ops for automated testing, container image builds, and deployment Containerise the data quality platform using Docker and manage deployment of container images across Business environments using Kubernetes Spin up and configure new container instances when onboarding new Business areas onto the platform Develop and maintain automated testing frameworks covering unit, integration, and end-to-end testing Design, build, and expose RESTful APIs to integrate the platform with upstream and downstream systems Collaborate with the Data Architecture team to ensure platform components are modular, platform-agnostic, and deployable across cloud and on-premise environments Work with Azure services including Azure Container Registry, Azure Kubernetes Service, Azure Key Vault, Azure Blob Storage, and Azure Dev Ops Develop and optimise data pipelines using Py Spark and SQL Server Support the integration of Databricks workloads into the platform architecture Ensure platform security standards are met including secret management, image vulnerability scanning, and access controls Contribute to technical documentation, runbooks, and onboarding guides for Business teams Required Skills & Experience:

8–12 years of experience in data engineering or platform engineering roles Solid hands-on experience with Azure services — AKS, ACR, Key Vault, Blob Storage,



Azure Dev Ops Proven experience containerising applications using Docker and orchestrating with Kubernetes Solid experience building and maintaining CI/CD pipelines in Azure Dev Ops Experience designing and building RESTful APIs Proficiency in Py Spark and SQL Server Experience with Databricks — job orchestration, cluster management, notebook development Strong understanding of software testing practices — unit testing, integration testing, end-to-end testing Experience with branching strategies, versioning, and release management Strong problem-solving skills and ability to work independently in a fast-paced environment Preferred Skills:

Understanding of Data Quality concepts — profiling, validation rules, DQ scoring, observability Experience with data quality tooling such as Informatica, Great Expectations, or similar Familiarity with Flask or similar Python web frameworks Experience with modular, microservice-style architecture design What We're Looking For:

Beyond technical skills, we are looking for someone who takes ownership, thinks like a platform engineer, and understands that the best infrastructure is the kind that Business teams never have to think about. The ideal candidate is equally comfortable writing a Dockerfile, debugging a failing pipeline, and discussing deployment governance with a non-technical stakeholder.

📌 Senior Data Engineer (Bengaluru)
🏢 TEKsystems
📍 Bengaluru