Top Tech Jobs & Startup Jobs

Designs, builds, and maintains scalable batch and streaming data pipelines, lakehouse medallion architectures, transformation layers, and data models. Develops PySpark and SQL solutions, integrates diverse data sources, implements data quality monitoring, and manages infrastructure as code. Troubleshoots pipeline and data incidents, participates in code reviews, collaborates with architects, and mentors junior and mid-level engineers.

Owns the lifecycle and roadmap of data products on a lakehouse platform. Defines requirements, schemas, SLAs, access controls, and documentation; collaborates with data engineers on delivery; tracks adoption, quality, and business value KPIs; manages catalogs and metadata; leads sprint planning, backlog grooming, capacity, and releases; and coordinates governance, security, and compliance policies.

Leads enterprise semantic layer, data, and AI platform architecture. Defines lakehouse, medallion, data model, data contract, metadata, schema governance, feature store, model lifecycle, and interoperability standards. Partners with product, engineering, platform, and data science teams to create secure, reusable foundations for analytics, machine learning, and AI applications. Translates business and governance requirements into scalable technical architectures and provides thought leadership across enterprise data and AI enablement.

Leads Dynata’s Consumer Intelligence product portfolio, defining strategy, roadmap, business cases, pricing, packaging, commercialization, and growth objectives. Transforms proprietary panel, survey, profile, and behavioral data into scalable commercial products. Conducts customer and market discovery, drives product development and adoption, aligns cross-functional stakeholders, and builds a product management team as the portfolio expands.

Leads enterprise AI transformation by developing the AI roadmap, prioritizing investments, managing initiative portfolios, and delivering measurable business value. Partners with executives and cross-functional teams to redesign operations, establish governance, evaluate technologies and vendors, drive adoption, and lead workforce change. Accountabilities include revenue growth, productivity, efficiency, customer experience, compliance, AI maturity, and value realization.

Leads graph analytics, identity resolution, forecasting, and predictive modeling initiatives for operational, commercial, and product decisions. Designs scalable graph data models, production-ready machine learning solutions, and time-series forecasts. Establishes validation, monitoring, governance, and experimentation practices while partnering with product, engineering, and platform teams. Translates complex analytical findings into business recommendations and provides technical leadership across graph science, forecasting, optimization, and decision-support capabilities.

Build and maintain CI/CD pipelines, infrastructure automation, Kubernetes-based data platform environments, and observability tooling. Manage Terraform and Helm deployments, cloud cost optimization, provisioning, policy enforcement, incident response, capacity planning, and operational documentation. Collaborate with data engineering and security teams to improve reliability, scalability, compliance, and engineering productivity across enterprise lakehouse platforms.

Design and implement enterprise data quality frameworks for lakehouse platforms. Build automated validation tests, dashboards, anomaly detection, observability solutions, and CI/CD quality checks. Manage data quality SLAs, profile new sources, resolve incidents, collaborate with governance and source owners, and educate engineering and product teams on quality standards.

Designs, builds, and maintains enterprise lakehouse data pipelines, transformation layers, and data models. Develops scalable batch and streaming solutions using Spark, Kafka, and Flink; implements medallion architecture, data quality monitoring, and infrastructure as code. Troubleshoots performance and data incidents, participates in code reviews, collaborates on data modeling, and mentors junior and mid-level engineers.

Owns the lifecycle, roadmap, and delivery of data products on a lakehouse platform. Defines requirements, schemas, SLAs, access controls, documentation, and KPIs; manages catalogs and metadata; leads sprint planning, backlog grooming, capacity, and releases; and coordinates with data engineering, governance, security, and compliance teams to ensure quality, adoption, discoverability, and business value.