Remote Data Entry Specialist – Enterprise Data Engineering & Analytics – Join talentflow

About talentflow

Talentflow

is a global leader in data‑centric solutions, empowering businesses to unlock the full potential of their information assets. With a portfolio that spans cloud data platforms, real‑time analytics, and AI‑driven insights, talentflow is at the forefront of the digital transformation wave. Our mission is simple: help organizations turn raw data into actionable intelligence that drives growth, efficiency, and innovation.

We pride ourselves on a culture that values curiosity, collaboration, and continuous learning. Whether you’re a seasoned data engineer or a passionate newcomer, talentflow offers an environment where ideas are nurtured, challenges are embraced, and career growth is a shared priority.

Role Overview

We are seeking a highly motivated

Remote Data Entry Specialist

to join our data engineering team. In this role, you will design, build, and maintain robust data pipelines that support analytics, reporting, and AI initiatives across the organization. You will work closely with data scientists, business analysts, and product managers to ensure that data flows seamlessly from source to destination, enabling real‑time decision making.

As a remote member of the talentflow team, you will enjoy the flexibility of working from anywhere in the United States while collaborating with a diverse, global group of experts. Your contributions will directly impact the quality and reliability of the data that powers talentflow’s flagship products and services.

Key Responsibilities

Data Pipeline Design & Development

• Architect and implement batch and real‑time data pipelines using Spark (PySpark), Azure Data Factory, and Airflow to ingest, transform, and load data from a variety of sources.

Data Quality & Governance

• Establish data validation rules, monitoring dashboards, and automated alerts to ensure data integrity and compliance with internal standards.

Performance Optimization

• Tune SQL queries, denormalized data models, and Spark jobs to achieve optimal throughput and low latency.

Documentation & Knowledge Sharing

• Create and maintain comprehensive documentation for data pipelines, schemas, and best practices; lead knowledge‑sharing sessions for cross‑functional teams.

Root Cause Analysis

• Investigate and resolve production incidents, implementing permanent fixes and preventive measures.

Collaboration & Leadership

• Partner with data scientists, product owners, and infrastructure engineers to translate business requirements into scalable data solutions.

Continuous Improvement

• Stay abreast of emerging data engineering tools and techniques; propose and pilot new technologies that enhance data processing capabilities.

Essential Qualifications

• Bachelor’s degree in Computer Science, Information Systems, or a related field (or equivalent professional experience).
• 7–10 years of experience designing and building large‑scale, high‑performance data architectures.
• Proficiency in

PySpark

and experience using Jupyter Notebooks, Colab, or Databricks for iterative development.

• Hands‑on experience with Azure Data Factory, Azure Synapse, or AWS Glue for orchestrating data workflows.
• Strong SQL skills and familiarity with denormalized data modeling for big data environments.
• Experience with NoSQL platforms (e.g., Cosmos DB, MongoDB) and at least 3 years of Spark development.
• Solid understanding of CI/CD pipelines and experience with tools such as Azure DevOps or GitHub Actions.
• Knowledge of semantic data concepts and data cataloging practices.
• Excellent analytical, problem‑solving, and communication skills.

Preferred Qualifications

• Experience with real‑time streaming technologies such as Kafka, Azure Event Hubs, or AWS Kinesis.
• Background in building self‑healing, fault‑tolerant data pipelines.
• Familiarity with SOA architecture and microservices design patterns.
• Certifications in Azure Data Engineer Associate, AWS Certified Data Analytics, or similar.
• Previous remote work experience in a distributed team environment.

Skills & Competencies

Technical Expertise

• Deep knowledge of data engineering principles, ETL/ELT processes, and cloud data platforms.

Collaboration

• Ability to work effectively with cross‑functional teams in a remote setting.

Adaptability

• Comfortable with evolving technologies and shifting priorities.

Leadership

• Willingness to mentor junior engineers and lead complex projects.