Top Tech Jobs & Startup Jobs

Develop Tableau dashboards, BI reports, and data visualizations for banking, financial services, and insurance systems. Gather requirements, analyze data lineage, support big data modeling, integrate BI solutions, document data structures, and identify process improvements through data-driven insights. Collaborate with business and IT teams to communicate findings and deliver actionable solutions.

Designs, deploys, and maintains scalable AWS cloud infrastructure. Responsibilities include managing compute, storage, networking, databases, landing zones, Control Tower, and SCPs; automating infrastructure with IaC; monitoring performance and costs; implementing security and compliance controls; supporting CI/CD integration; collaborating with development and DevOps teams; and troubleshooting infrastructure issues.

Develop and maintain data lake and data mart architecture, translate client requirements into migration plans, extract and migrate data while preserving integrity and security, create data visualizations, and collaborate with QA to resolve defects. The role requires client-focused travel and/or relocation to unanticipated U.S. locations as project needs dictate.

Design, develop, optimize, and maintain scalable AWS data processing and ETL/ELT pipelines using Python, PySpark, Apache Spark, and SQL. Responsibilities include data extraction, transformation, validation, performance tuning, integration across databases, APIs, and cloud storage, monitoring, error handling, and data quality management. The role collaborates with engineering, architecture, analytics, and business teams while following Git, Agile, and software development best practices.

Design and optimize scalable ETL/ELT pipelines and modern data platforms using Databricks, PySpark, Azure Data Factory, ADLS, Delta Lake, and Synapse Analytics. Develop data ingestion and transformation workflows, optimize Spark performance, implement CI/CD automation, and uphold data quality, security, and governance standards. Collaborate with architects, analysts, and business stakeholders on cloud-based data engineering solutions.

Develop and implement client data systems, including database environments, data migration processes, and secure, efficient data access solutions. Contribute to data-system planning for stakeholder approval and collaborate directly with clients on projects. The role requires travel and/or relocation to unanticipated U.S. locations based on client and project needs. Technologies include Talend Enterprise Edition, ETL, Java, SQL, and PL/SQL.

Develop and maintain data lake and data mart architecture, translate client requirements into migration plans, extract and migrate data while preserving integrity and security, and create data visualizations. Collaborate with QA teams to resolve defects and validate systems. The role requires working with ETL, SQL, Oracle, Informatica, Teradata, and Power BI, plus travel or relocation to client project locations across the United States.

Develop and maintain Oracle PL/SQL features, including functions, procedures, triggers, packages, and performance tuning. Troubleshoot defects and operational incidents, write unit tests, automate code before QA, follow engineering practices, participate in Scrum ceremonies, and mentor junior team members. The role requires strong debugging, analytical, organization, and problem-solving skills in a hybrid work environment.

Build and deploy production-grade AI and agentic systems for enterprise customers. Responsibilities include multi-agent orchestration, retrieval pipelines, workflow automation, foundation-model integrations, monitoring, evaluations, guardrails, CI/CD, incident troubleshooting, and ongoing production ownership. The role embeds with customer engineering teams, translates ambiguous business needs into scalable AI solutions, develops reusable architectures, and mentors engineers.

Build scalable ETL/ELT pipelines in Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, Workflows, and Unity Catalog. Ingest batch and streaming data across medallion architecture layers, develop transformations and data quality rules, and implement orchestration, monitoring, alerting, and automation. The role requires cloud data engineering, data modeling, performance tuning, CI/CD, and strong knowledge of lakehouse architecture, governance, and distributed systems.