Data Engineer

Why This Role Exists

We are building the next generation of an AI-driven data and intelligence platform within a high-volume, multi-tenant SaaS environment. Every transaction and system integration generates valuable data, and our ability to turn that information into reliable reporting, analytics, and AI-powered insights depends on a modern, scalable data foundation.

We are looking for a Data Engineer to architect, build, and operate the analytical data ecosystem that supports today's reporting needs while powering tomorrow's AI and intelligence capabilities.

This is a hands-on builder role on a small, highly collaborative engineering team. You will own data pipelines end to end—from ingestion and transformation through the models, APIs, reports, and AI applications that consume the data. You'll also have a meaningful voice in the architectural decisions shaping the future of the platform.

Scope & Scale

You'll work in a complex production environment that includes:

• Thousands of tenants with isolated databases and tenant-specific configurations.
• Data pipelines spanning multiple integration domains and connectivity methods, including REST, SOAP, and event-driven architectures.
• A large semantic layer supporting business intelligence, self-service reporting, operational teams, and strategic partners.
• A modern lakehouse architecture using bronze, silver, and gold layers on Apache Iceberg.
• Production AI capabilities whose tools, context, and intelligence depend directly on the quality and reliability of the data you provide.

What You'll Own

AWS Data Workloads

Architect, build, and operationalize pipelines and storage using technologies such as AWS Glue, Athena, S3, DynamoDB, Lambda, and Step Functions. Depending on the workload, you'll also work with technologies such as Timestream, Flink, and Kafka.

Lakehouse Architecture

Build and optimize bronze, silver, and gold data layers using Apache Iceberg. You'll own table design, partitioning, compaction, and storage-format decisions involving technologies such as Parquet, Iceberg, and ORC.

Data Models & Access

Develop trusted data models used by analysts, reports, applications, and AI capabilities. Build secure data-access APIs using Java and/or Python and implement real-time streaming where appropriate.

Pipeline Reliability

Establish monitoring, alerting, logging, freshness checks, and completeness controls that identify pipeline problems before they impact users.

Anomaly Detection & Data Quality

Apply statistical and machine-learning approaches to monitor data quality and integration activity, allowing spikes, drops, anomalies, and silent failures to surface quickly.

Forecasting & Predictive Analytics

Develop models in Python using technologies such as scikit-learn, pandas, and NumPy to support forecasting and predictive capabilities.

Governance & Security

Help ensure data remains secure and appropriately isolated through SSL/TLS, client authentication, access auditing, and tenant-level controls.

Cross-Functional Delivery

Partner with engineers, product managers, analysts, and AI-focused teams to translate business and technical requirements into scalable, secure, high-quality data solutions.

What Success Looks Like

First 90 Days

You'll learn the platform, integration domains, and semantic layer well enough to trace key metrics back to their source systems.

You'll take ownership of production reporting pipelines, strengthen freshness and completeness monitoring where needed, and ship your first production change to the lakehouse.

You'll also develop a clear technical point of view regarding the ingestion and modeling patterns the organization should standardize going forward.

First Year

The lakehouse architecture will be operating in production with mature bronze, silver, and gold layers that you helped build or strengthen.

Production pipeline issues will increasingly be identified through proactive monitoring rather than user reports.

You'll have delivered at least one forecasting or anomaly-detection model that is actively used within the product or business operations.

Just as importantly, the architectural and engineering patterns you establish will provide a foundation future data engineers can build upon.

Hands-On Expectations

This is a highly technical, hands-on engineering position.

Your time will generally be divided among:

• 70%: Building and operating pipelines, data models, APIs, and monitoring.
• 20%: Data architecture and technical design with platform and AI teams.
• 10%: Working with analysts and product teams on requirements and enablement.

You should expect to write and ship production code every week.

What You Bring

Must Have

• Extensive experience architecting, building, and operationalizing data workloads on AWS, particularly Athena, Glue, S3, and DynamoDB.
• Deep understanding of high-performance analytical data platforms and distributed systems.
• Strong knowledge of modern data formats and technologies such as Parquet, Apache Iceberg, and ORC.
• Expertise building services and APIs on top of data platforms using Java and/or Python.
• Strong experience with data modeling, data-access API design, and production monitoring and alerting.
• Hands-on Python experience for analytics and machine learning using pandas, NumPy, and scikit-learn.
• Experience applying statistical or machine-learning approaches to anomaly detection.
• Ability to clearly document pipelines, data definitions, architectural decisions, and production incidents for both technical and business audiences.
• Strong problem-solving and collaboration skills with the ability to thrive on a fast-moving, highly accountable engineering team.

Strongly Preferred

• Lakehouse experience using Apache Iceberg, Delta Lake, or Apache Hudi, including medallion-style data architectures.
• Production experience with real-time streaming technologies such as Flink, Kafka, or Kinesis.
• Experience building data infrastructure that supports LLM agents or AI-powered product features, including tool-facing APIs, retrieval architectures, or feature stores.
• Experience with multi-tenant SaaS data architecture, including tenant isolation, row-level security, and tenant-specific configuration.

Nice to Have

• Experience working with complex transactional or highly integrated SaaS platforms.
• Power BI, Amazon Quick, or similar semantic modeling experience.
• Experience with dbt-style transformation workflows.
• Infrastructure-as-code experience using AWS CDK or CloudFormation.
• Experience implementing CI/CD practices for data pipelines.

Technology Environment

Data Platform:

Amazon Athena / Presto / Trino SQL, AWS Glue, Amazon S3, Apache Iceberg, DynamoDB, Lambda, Step Functions

Streaming & Time Series:

Timestream, Flink, Kafka

Languages & Machine Learning:

Python, Java, SQL, pandas, NumPy, scikit-learn

Reliability & Governance:

CloudWatch, OpenTelemetry, data freshness and completeness monitoring, SSL/TLS, access auditing

Analytics & AI:

Business intelligence platforms, semantic modeling tools, LLM/AI agents, predictive analytics, and AI-enabled data applications

How You'll Use AI

AI is an active part of the engineering workflow, not an experimental side project. You'll be expected to use modern AI development tools to increase engineering velocity while maintaining a high standard for accuracy and production quality.

Examples include:

• Drafting and testing pipeline code, SQL, and data-quality checks with AI assistance, followed by validation against trusted production data.
• Generating lineage and documentation directly from data models to keep mappings between source systems, reporting, and AI applications current.
• Summarizing pipeline incidents into useful root-cause documentation and actionable runbooks.
• Building the tables, APIs, data models, and context layers that production AI capabilities depend on.
• Rapidly prototyping forecasting and anomaly-detection approaches and evaluating their effectiveness against real-world outcomes.

The goal isn't simply to use AI tools—it's to build the reliable data foundation that production AI depends on.

Compensation & Benefits

The anticipated base salary range for this position is $160,000–$190,000, depending on experience and qualifications, plus an equity component.

The organization also offers a comprehensive benefits package that may include:

• Employer-paid health, dental, and vision coverage.
• Flexible hybrid and remote work options.
• 401(k) retirement plan with company match.
• Generous paid time off and paid holidays.
• Employer-paid short-term and long-term disability coverage.
• Employer-paid life and AD&D insurance.
• Mental health and wellness resources.
• Additional voluntary employee benefits.

The Opportunity

This role offers the opportunity to help shape a modern data platform rather than simply maintain an established one. You'll work at the intersection of data engineering, cloud architecture, analytics, machine learning, and production AI, with direct ownership over systems that have a meaningful impact on the broader product.

If you enjoy building scalable data systems, solving complex integration and reliability problems, and creating the foundation that allows analytics and AI products to succeed, this is an opportunity to make a significant technical impact.

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