Jobs at DATAECONOMY

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.

Designs and builds secure, production-grade Generative AI and agentic AI applications using Python, LLMs, cloud services, and AI frameworks. Responsibilities include developing scalable AI pipelines, integrating Databricks data with AWS services, applying RAG and vector databases, evaluating models, implementing guardrails, researching emerging tools, improving application infrastructure, and guiding team members.

Build scalable production-grade ETL/ELT pipelines on Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, and Workflows. Develop data ingestion, transformations, quality rules, orchestration, monitoring, and automation across Bronze, Silver, and Gold layers. Apply dimensional data modeling, streaming, cloud platforms, CI/CD, governance, security, performance tuning, and lakehouse architecture. Explain technical concepts and architecture decisions using real project examples.

Designs and leads enterprise Snowflake data architectures, cloud data platforms, ELT/ETL pipelines, and AI-powered applications. Builds RAG, semantic search, Cortex Agents, vector embedding, and Cortex AI Function solutions using Snowpark, Python, SQL, and dbt. Establishes secure, governed, and cost-optimized data environments, mentors engineering teams, leads architecture reviews, and partners with stakeholders on scalable data and generative AI solutions.

Design and operate AWS production-serving infrastructure for fine-tuned LLMs. Deploy models using vLLM, TensorRT-LLM, or Triton; optimize inference and GPU utilization; implement shadow, canary, A/B, staged rollout, and automated rollback workflows; and build monitoring for latency, throughput, quality, and cost. The role also covers auto-scaling, high availability, Kubernetes-based ML workloads, continuous batching, quantization, and KV-cache management.

Build and maintain AWS-based pipelines that transform raw production traces into high-quality LLM training datasets. Responsibilities include ingestion, deduplication, format conversion, quality filtering, identity-aware train/test splitting, schema validation, sampling, and continuous retraining-data processing. The role requires strong Python data engineering skills, large-scale JSONL processing, Hugging Face Datasets, Arrow-based storage, AWS services, and familiarity with conversational and tokenizer-specific data formats.

Fine-tune open-source LLMs from experimentation through production on AWS. Run SFT and alignment methods including GRPO and DPO, debug distributed multi-GPU training, resolve loss and memory issues, and collaborate with evaluation and data engineering teams to improve accuracy. Scale models from 8B to 70B parameters based on offline metrics.

Develop and maintain enterprise-grade Java applications using Spring Boot, Hibernate/JPA, REST APIs, and microservices architecture. Build responsive interfaces with React or Angular, integrate databases and third-party services, optimize performance, implement security, and automate CI/CD pipelines. Troubleshoot production issues, participate in code reviews, mentor junior developers, and collaborate with cross-functional teams to deliver scalable, reliable solutions.

Design, develop, and optimize scalable Databricks data engineering solutions. Build ETL/ELT pipelines, manage clusters, implement Lakehouse, Delta Lake, Delta Live Tables, Medallion Architecture, and Unity Catalog. Optimize Spark jobs for performance and cost, monitor pipelines, resolve data quality issues, and apply DevOps and CI/CD practices while collaborating with architects, analysts, and stakeholders.

Build and manage the full lifecycle for self-hosted LLMs, including training data pipelines, SFT and DPO fine-tuning, evaluation, benchmarking, quantization, deployment, monitoring, and continuous improvement. Deploy models on AWS GPU infrastructure and SageMaker, automate ML CI/CD, and manage Docker and Kubernetes workloads on EKS. Implement A/B, canary, and shadow deployments with automated promotion and rollback based on performance and operational metrics. Collaborate with data science, ML engineering, and DevOps teams.

Analyze insurance policy, premium, claims, billing, and financial data; build KPI dashboards and reports; write and optimize SQL; validate and model datasets; identify trends, anomalies, fraud indicators, and leakage; and translate business questions into clear recommendations for underwriting, actuarial, claims, finance, and other stakeholders. The role supports pricing, reserving, portfolio management, retention, and loss-ratio analyses.