Top Tech Jobs & Startup Jobs

Build and operate cloud-native Azure data pipelines from source ingestion through Bronze-Silver-Gold transformation layers. Develop Fabric Dataflow Gen2 pipelines, Delta Tables, Synapse SQL evaluation logic, and event-driven integrations using Azure Event Hub and Event Grid. Perform data quality validation, schema reconciliation, lineage verification, and parallel-run testing across multiple domains. Extend configuration-driven frameworks and support sprint-based delivery across concurrent workstreams.

Leads the design, development, and deployment of scalable cloud-native data pipelines for enterprise analytics and reporting. Establishes data engineering standards, governance, security, privacy, and compliance practices. Makes architectural decisions, mentors engineers, conducts reviews, translates business needs into technical solutions, and owns end-to-end data initiatives in an Agile environment.

Designs, develops, and maintains Snowflake-based data solutions, including optimized SQL queries, stored procedures, ETL/ELT pipelines, and data transformations. Performs validation, troubleshooting, performance tuning, and implements data governance, security, and scalability best practices. Collaborates with engineers, analysts, architects, and stakeholders to deliver cloud data warehouse solutions.

Build and scale reusable data modernization assets, including discovery tools, code-generation frameworks, converters, validation patterns, playbooks, runbooks, and estimation aids. Consolidate delivery learnings, drive accelerator adoption across teams, measure reuse, and train delivery pods on accelerators and AI-assisted engineering. Lead modernization programs involving dbt, Snowflake, migration planning, reconciliation, parallel runs, and cutover execution.

Designs, develops, and executes automated UI, API, integration, backend, functional, and performance tests. Responsibilities include expanding automation frameworks and regression coverage, creating testing utilities, validating cloud-based and multi-tier applications, analyzing defects, and supporting CI/CD continuous testing. The role collaborates with developers and QA engineers in Agile/Scrum teams, participates in sprint ceremonies, manages defects in JIRA, and communicates testing progress and risks.

Architect and build enterprise-scale MLOps and AI platforms supporting thousands of production ML models and high-volume distributed workloads. Define architectures for feature stores, MLflow, model registries, distributed training, deployment, inference, monitoring, governance, lineage, explainability, and automated retraining. Establish secure, scalable Databricks Lakehouse standards using Unity Catalog, Delta Lake, DLT, and Mosaic AI. Partner with data teams, optimize reliability and cost, and mentor engineering teams.

Leads enterprise data modernization initiatives by designing scalable cloud data architectures, modernizing pipelines and ETL/ELT frameworks, directing migrations, and guiding engineering teams. Establishes standards for modeling, governance, metadata, lineage, security, reliability, observability, and data quality. Partners with application, cloud, analytics, and business stakeholders to align integrations and data flows. Produces technical designs and migration documentation while supporting testing, production readiness, deployment, and handover activities.

Build and optimize scalable cloud data solutions, including ingestion, transformation, curation, and ETL/ELT pipelines. Apply data modeling and warehousing best practices, automate workflows, monitor pipeline health, troubleshoot quality and performance issues, and optimize reliability and cost. Collaborate with Cloud Solution Architects, lead code reviews, follow controlled development procedures, and support CI/CD across Agile and Waterfall environments.

Design and deliver enterprise AI automation solutions across Microsoft 365. Build Copilot agents, Power Platform workflows, RAG-based knowledge systems, MCP server integrations, and AI-to-system connections. Apply prompt engineering and reusable workflow patterns while integrating CRM, ERP, and internal systems. Ensure solutions are secure, compliant, scalable, and governed, collaborating with stakeholders to improve productivity and reduce manual effort.

Lead analysis of large cross-platform TV and streaming datasets for marketing attribution, audience segmentation, media mix modeling, and optimization. Build queries, validate data, define analytical logic, and support scalable data products and automated workflows. Collaborate with engineering, marketing, research, and acquisition teams while maintaining data quality, governance, documentation, privacy, and legal compliance.