ETL Developer

Bright Vision Technologies•Overland Park, KS

2d•$115,000 - $175,000•Remote

About The Position

Bright Vision Technologies is seeking an experienced ETL Developer to design, develop, and optimize enterprise-scale data integration solutions for business intelligence, analytics, regulatory reporting, and cloud data modernization. The role focuses on building high-performance ETL pipelines that ingest, transform, validate, and deliver data from diverse operational systems to enterprise data warehouses and cloud analytics platforms. The developer will work with data architects, data engineers, business analysts, cloud engineers, and BI developers to create scalable integration frameworks, automate workflows, and deliver trusted data. This is a hands-on engineering role covering data architecture, ETL development, cloud migration, performance optimization, production support, and continuous improvement. The team supports initiatives across financial services, healthcare, insurance, and retail, including warehouse modernization, cloud migration, real-time ingestion, MDM, regulatory reporting, lakehouse architecture, and high-volume enterprise data pipelines. The position is 100% remote in the U.S., full-time, direct W2, with a stated annual salary range of $115,000–$175,000. The employer cannot sponsor new H-1B visa petitions; U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply.

Requirements

Five or more years of professional experience developing enterprise ETL solutions.
Hands-on experience with ETL platforms such as Informatica PowerCenter, SSIS, Talend, IBM DataStage, or Pentaho.
Advanced SQL skills with SQL Server, Oracle, PostgreSQL, Snowflake, or Teradata.
Strong understanding of relational databases, dimensional modeling, star and snowflake schemas, and data warehousing principles.
Experience designing ETL workflows with complex transformations, joins, aggregations, lookups, and data validation.
Hands-on experience with CDC, SCD Type 1 and Type 2, and incremental loading techniques.
Experience with cloud data platforms such as Azure Data Factory, AWS Glue, or Google Cloud Data Fusion.
Experience integrating JSON, XML, CSV, Parquet, and Avro data.
Familiarity with Python, PowerShell, or Shell scripting for ETL automation.
Experience scheduling workflows with Control-M, Autosys, Apache Airflow, Azure Data Factory, or enterprise schedulers.
Experience with Git, Azure DevOps, Jenkins, or other CI/CD tools for ETL deployments.
Strong debugging, troubleshooting, and ETL performance-tuning skills.
Experience working with Agile Scrum teams and enterprise SDLC processes.

Nice To Haves

Experience with Snowflake, Azure Synapse Analytics, Amazon Redshift, or Google BigQuery cloud data warehouses.
Knowledge of Apache Spark, Databricks, PySpark, or distributed data processing frameworks.
Experience implementing enterprise Master Data Management and Data Quality solutions.
Familiarity with Apache Kafka, Azure Event Hub, or Amazon Kinesis.
Experience developing metadata-driven ETL frameworks and reusable integration components.
Knowledge of Data Vault 2.0 architecture and modern data lakehouse concepts.
Familiarity with DevOps practices for data engineering and automated ETL deployments.
Experience with Power BI, Tableau, or Qlik for downstream reporting integration.
Understanding of data governance frameworks, lineage, cataloging, and regulatory compliance.
Cloud certifications in Azure, AWS, Snowflake, or Informatica.

Responsibilities

Design, develop, and maintain enterprise ETL/ELT pipelines for large-scale data integration projects.
Build scalable workflows to ingest data from relational databases, cloud applications, APIs, flat files, and enterprise systems.
Develop transformations to cleanse, standardize, validate, and enrich large datasets.
Design and optimize batch and near-real-time processing pipelines for business-critical reporting and analytics.
Implement Slowly Changing Dimensions (SCD), Change Data Capture (CDC), incremental loading, and data reconciliation processes.
Develop reusable ETL frameworks, metadata-driven workflows, and parameterized jobs.
Perform source-to-target mapping, data profiling, and data quality analysis.
Optimize ETL performance through SQL improvements, partitioning, indexing, and parallel processing.
Monitor workflows, troubleshoot production failures, and implement alerting and recovery mechanisms.
Collaborate with cloud engineers to migrate legacy ETL workloads to Azure or AWS.
Participate in data architecture discussions, code reviews, deployment planning, and production support.
Ensure data security, governance, auditability, and regulatory compliance across data pipelines.