Data Engineer

Description

We are looking for an experienced Data Engineer to jcreate and enhance dependable data platforms that support HR and enterprise analytics, partnering closely with analysts, engineering leads, and business stakeholders on site. The position focuses on building scalable pipelines, strengthening data quality, and enabling trusted insights that improve operational decision-making across the organization.

Responsibilities

Design, develop, and maintain scalable data pipelines that integrate information from files, APIs, databases, replicated sources, and streaming inputs.

Build and support modern data environments across warehouses, data lakes, and lakehouse architectures to meet analytics and reporting needs.

Partner with business analysts, HR stakeholders, and technical team members to translate data requirements into reliable engineering solutions.

Improve data quality, lineage, and governance by applying metadata-driven practices and implementing controls that increase trust in enterprise datasets.

Lead end-to-end delivery of data engineering initiatives, from solution design and development through testing, deployment, and operational support.

Manage orchestration, scheduling, and monitoring of data workflows to ensure stable performance and timely delivery of critical datasets.

Apply DevOps practices such as version control, automated testing, and CI/CD processes to increase deployment quality and team collaboration.

Use Python, SQL, and cloud-based tools to automate data processing, optimize performance, and support scalable distributed workloads.

Implement data protection measures, including masking, encryption, anonymization, and role-aware access design, especially for sensitive workforce information.

Support curated analytical datasets and reporting solutions by collaborating with downstream users on trusted models and enterprise data products.

Requirements

7+ years of experience in data engineering, data modeling, or related data platform development work. At least 2 years of hands-on experience working with Snowflake in production data environments. Strong background building and supporting ETL or ELT pipelines using SQL and Python. Experience with data warehousing, data lakes, and modern cloud-based data architecture patterns. Familiarity with Microsoft Azure services used for storage, integration, processing, orchestration, and security. Proven ability to lead full lifecycle data engineering or reporting projects with minimal supervision.

Strong analytical and troubleshooting skills, including root cause analysis and performance optimization. Effective communication skills with the ability to work across technical teams, analysts, and business partners.

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