Senior Data Analyst (Cloud / Python / SQL)

Senior Data Analyst (Cloud / Python / SQL)

ECLARO: A quick Summary
ECLARO is an award-winning professional services firm headquartered in New York City and operating in the U.S., Canada, UK, Ireland, Australia and the Philippines. We are dedicated to a singular purpose: providing the Right People to meet every client's needs and solve business challenges through strategic staffing, permanent placement, custom outsourcing & offshoring. Utilizing our proprietary TRINIT-E® Service Maturity Model, we help clients implement programs to promote innovation, automation and process improvement.

Position Summary
The Data Analyst operates at the intersection of analytics, governed data platforms, and AI-enabled workflows. In addition to defining KPIs and translating enterprise data into actionable insight, this role is expected to work hands-on with modern AWS data-platform services, transformation/orchestration tooling, governed lake/lakehouse patterns, DataOps, and AI/ML data enablement. The ideal profile is closer to a senior technical Data Analyst / Analytics Engineer with strong AWS data-platform depth than a dashboard-only BI analyst.

KeyResponsibilities

• Partner with business units to define data standards, data-quality rules, KPI definitions, baselines, target outcomes and governed reporting requirements
• Design and implement data-platform and analytics solutions on AWS using Lake Formation, Glue, Athena, IAM and related AWS data services.
• Design modern data lakes/lakehouses, governed publishing zones, and batch/streaming analytics pipelines on AWS.
• Build and maintain transformation pipelines and reusable analytical data models using dbt, Python and SQL.
• Use Apache Airflow or Amazon MWAA to orchestrate analytics/data workflows, including dependencies, retries, scheduling, monitoring and operational support.
• Use Spark/PySpark or comparable distributed-processing technologies for large-scale data preparation and analytics workloads.
• Build secure data-governance capabilities using Lake Formation, Glue Data Catalog, IAM, metadata, lineage, quality and access-control standards.
• Design or support enterprise feature stores for offline training and online/batch inference use cases.
• Support batch and live inference workflows using AWS SageMaker and Bedrock services, partnering with AI/ML teams on data and measurement requirements.
• Build CI/CD and DataOps pipelines for analytics/data code and infrastructure use GitLab or comparable platforms for testing, release management and deployment.
• Create or maintain infrastructure as code using AWS CDK or CloudFormation where needed for analytics/data-platform delivery.
• Analyze workflow performance, productivity, quality, adoption, anomalies and business outcomes translate results into actionable recommendations.
• Build reporting views and dashboards using governed data products and communicate findings to technical and non-technical stakeholders.

Required Qualifications

• 5+ years of experience in data analysis, analytics engineering, BI, data-platform analytics or a related technical data role.
• Strong SQL and Python with hands-on experience preparing, transforming, validating and analyzing enterprise data.
• Extensive hands-on AWS data-platform experience, including strong practical experience with Lake Formation, Glue, Athena, IAM and related AWS data services.
• Hands-on Apache Airflow or Amazon MWAA, dbt, and distributed processing with Spark/PySpark.
• Experience designing or working deeply with modern data lakes/lakehouses, governed publishing zones, and batch/streaming pipelines.
• Experience with data quality, metadata, lineage, monitoring, security and governance in enterprise environments.
• Experience with Git-based CI/CD/DataOps delivery and automated testing/release practices.
• Strong communication skills and ability to translate technical data-platform concepts and analysis into business decisions.

Preferred / Differentiating Qualifications

• Experience designing or supporting enterprise feature stores and training/inference data flows.
• Experience with SageMaker and/or Bedrock for batch or live inference.
• Experience with AWS CDK or CloudFormation for infrastructure as code.
• Experience with Databricks, Redshift, QuickSight, Power BI, Tableau or other enterprise analytics platforms.
• Experience in financial services, insurance, banking, fintech, wealth, payments or another regulated environment.
• Experience supporting US/global teams, distributed operations, and senior business stakeholders.
• Exposure to AI-enabled workflow transformation, responsible AI controls, or regulated analytics/AI use cases.