ETL Engineer

Description

Location: Hybrid in St Louis, MO

Job Type: Contract-to-hire

Company: The name of our partner organization will be disclosed during the interview process. This is not a direct role with LaunchCode; it is a position through LaunchCode, working with one of our partner companies.

Disclaimer: We are unable to provide work sponsorship for this role. We are unable to consider candidates for this role who have a current or future work sponsorship need (this includes those holding extended OPT Visas).

The Data Engineer / ETL Developer designs, builds, and supports reliable data pipelines that move and transform information from source systems into curated data platforms for reporting, analytics, operational insights, and cloud management use cases. This role is primarily aligned to AWS-based data engineering, while also supporting the organization's expanding ownership and use of Azure data and cloud services.

The role sits at the intersection of Cloud Services and Data & Analytics, partnering with business stakeholders, analysts, architects, application teams, and platform teams to deliver trusted, scalable, secure, and cost-aware data solutions. The position is expected to first understand what stakeholders are trying to accomplish, clarify requirements and data needs, and then work with the appropriate technical and business partners to translate those needs into well-designed data pipelines and integration patterns.

This role contributes across the full data lifecycle, including ingestion, transformation, orchestration, data quality, monitoring, documentation, and normal-hours production support. It is expected to support timely troubleshooting, root-cause analysis, and continuous improvement during normal support hours, without creating an after-hours emergency response expectation for dashboard or reporting issues.

KEY RESPONSIBILITIES

• Design, develop, and maintain batch and near-real-time ETL/ELT pipelines across AWS, Azure, and on-premises data sources, applying robust transformation logic, scalable engineering patterns, and reusable development practices.
• Partner directly with stakeholders to understand business objectives, reporting needs, operational questions, and pain points before moving into technical design. Work with architects, analysts, application teams, and platform teams to clarify requirements and translate them into source-to-target mappings, technical designs, and production-ready data workflows.
• Build and support pipelines using the tooling already in use across the environment, including AWS Glue, AWS Lambda, AWS Step Functions, dbt, Snowflake, PostgreSQL, S3, Athena, SQL, Python, PySpark, APIs, and related AWS technologies.
• Implement data quality checks, validation routines, reconciliation processes, logging, and monitoring to ensure accuracy, completeness, timeliness, and traceability of enterprise data assets.
• Troubleshoot pipeline failures, data issues, and job performance problems during normal support hours. Improve reliability through better alerts, runbooks, automation, query tuning, job optimization, and operational follow-through.
• Implement governance and security requirements within the data pipeline lifecycle by applying approved data handling standards, access controls, lineage expectations, retention requirements, privacy considerations, and compliance obligations.
• Design and operate pipelines with awareness of cloud cost, resource usage, performance, and operational efficiency. Partner with Cloud Services and FinOps resources to understand cost drivers, evaluate design tradeoffs, avoid unnecessary compute or storage waste, and support data needed for cloud financial and operational reporting.
• Document pipeline architectures, data flows, transformation rules, operational dependencies, support procedures, and known limitations so that solutions are maintainable and supportable by the broader team.
• Contribute to continuous improvement of Cloud Services and Data & Analytics practices by identifying recurring issues, reducing manual work, improving standards, and helping mature the reliability and transparency of the data platform.

REQUIRED QUALIFICATIONS

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field; equivalent practical experience will be considered.
• 4 years of experience in data engineering, ETL/ELT development, or data integration roles supporting analytics, reporting, operational data, or enterprise platforms.
• Strong proficiency in SQL and at least one modern programming language used for data engineering, such as Python or PySpark.
• Hands-on experience with ETL/ELT tooling, orchestration frameworks, and cloud-based data services such as AWS Glue, Lambda, Step Functions, dbt, or similar technologies.
• Experience working with modern data platforms and storage technologies such as Snowflake, PostgreSQL, S3, Athena, or comparable environments.
• Working knowledge of data quality practices, dimensional modeling concepts, version control, CI/CD processes, and production support for data pipelines.
• General knowledge of AWS services and cloud data engineering patterns, with interest in Azure data and cloud services as the organization's Azure ownership expands.
• Ability to communicate clearly with both technical and non-technical stakeholders, ask effective discovery questions, explain trade-offs, and help clarify business needs before recommending technical solutions.

PREFERRED BACKGROUND AND WORKING STYLE

• Experience in insurance, reinsurance, financial services, or other highly regulated environments with strong expectations for data governance, privacy, auditability, and controls.
• Familiarity with data warehouse design, metadata management, lineage documentation, data observability, and cloud data platform operations.
• Experience supporting cloud operational, financial, or governance datasets, including usage, cost, budget, configuration, vulnerability, inventory, or platform-management metrics.
• Ability to work across Cloud Services, Data & Analytics, Architecture & Engineering, Finance, application teams, and business stakeholders in a matrixed environment.
• Demonstrated curiosity, ownership, and continuous-learning mindset with a focus on operational excellence, maintainability, cost awareness, and scalable design.

DATA ENGINEER / ETL DEVELOPER OWNERSHIP BOUNDARIES

The Data Engineer / ETL Developer is accountable for designing, building, operating, and improving enterprise data pipelines and integration practices. The role partners across Cloud Services, Data & Analytics, architecture, infrastructure, application, governance, and business teams, while partner teams retain ownership of business definitions, platform strategy, formal governance decisions, and downstream reporting priorities.

The Data Engineer / ETL Developer owns:

• Pipeline design, development, testing, deployment support, and normal-hours operational support
• Source-to-target mappings, transformation logic, orchestration, and technical implementation details
• Data quality checks, validation, reconciliation, monitoring, logging, and alerting patterns
• Technical documentation, data flow documentation, operational runbooks, and reliability improvements
• Stakeholder discovery, requirements clarification, and technical partnership for data integration delivery
• Cost-aware pipeline design, efficient use of cloud resources, and support for cloud operational and financial data needs
• Implementation of approved data handling, security, lineage, access-control, and compliance expectations within pipelines

The Data Engineer / ETL Developer does not own:

• Final business ownership of data definitions, KPIs, metric interpretation, or reporting conclusions
• Final enterprise architecture standards, platform strategy, or cloud service-selection decisions
• Application system ownership for source platforms, upstream data entry, or business processes
• Formal governance ownership for enterprise policy, risk acceptance, audit sign-off, or regulatory interpretation
• Downstream analytics product ownership, dashboard prioritization, or business-prioritization decisions
• After-hours emergency response for dashboards, reporting issues, or non-critical data requests unless separately defined

WHAT SUCCESS LOOKS LIKE

Success in this role means stakeholders have greater confidence in the reliability, quality, and transparency of the data they use to make decisions. Pipelines are well-designed, documented, monitored, and supportable, with fewer recurring failures and less manual intervention over time. The role is also successful when Cloud Services and Data & Analytics teams have better visibility into cloud, operational, financial, and enterprise data flows across AWS and Azure. The Data Engineer / ETL Developer helps turn stakeholder needs into practical, secure, scalable, and cost-aware data solutions while ensuring ownership boundaries are clear and partner teams remain accountable for business definitions, platform strategy, and governance decisions.

Leadership takeaway: The Data Engineer / ETL Developer owns the engineering, operational discipline, stakeholder discovery, and cost-aware pipeline practices required to deliver trusted enterprise data solutions, while partner teams retain business, platform, architecture, and formal governance ownership.

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