Data Lakehouse Integration Engineer

Job Description

We are seeking an experienced data integration engineer to provide temporary coverage for a key member of the Data Lakehouse team during an extended leave period.

The primary responsibility of this role is to onboard enterprise data sources into our Databricks Lakehouse environment and drive those integrations from discovery through production readiness.

This is not purely a pipeline-development role. The engineer will work across application teams, data engineering, infrastructure, networking, security, and governance to identify dependencies, resolve blockers, and ensure that new data sources can be reliably and securely integrated into the Lakehouse.

What You’ll Do Own Data Source Onboarding

Drive new data-source integrations from initial discovery through operational ingestion. Responsibilities include:

• Understand the source system, available interfaces, data structures, volumes, and update patterns
• Work with application owners and SMEs to determine which data should be ingested
• Define appropriate ingestion and historical backfill approaches
• Validate ingestion completeness, reliability, and operational readiness
• Troubleshoot issues across the full path from source system to Lakehouse Our current environment includes a mix of databases, SaaS applications, APIs, object storage, file transfers, and event-streaming platforms.

Coordinate Infrastructure and Security Dependencies Many integrations require work outside Databricks. You will help drive those dependencies rather than treating them as external blockers. Examples include:

• Network connectivity and firewall changes
• Cloud IAM and service-account configuration
• Security and governance reviews You should be comfortable working directly with networking, infrastructure, cloud, security, and application teams to diagnose issues and move integrations forward.

Work Within the Databricks Lakehouse Platform Support ingestion and governance within our Databricks environment, including:

• Data ingestion pipelines and workflows
• Catalog and schema organization
• Access controls and permissions
• Connector configuration
• Operational troubleshooting Our environment uses Databricks and AWS extensively, but we care more about strong platform and data-engineering fundamentals than experience with every specific feature we use.

Lead Technical Discovery Work with SMEs and source-system owners to translate business requests into implementable ingestion requirements. You will help determine:

• What data is required

• Where it resides

• How it can be accessed

• How frequently it changes

• What historical data is required

• What security or governance constraints apply

• What dependencies must be resolved before implementation Drive Work Across Teams This role requires strong technical ownership. You will:

• Facilitate technical working sessions

• Maintain clear ownership of open dependencies

• Track onboarding progress

• Escalate blockers early

• Communicate status clearly

• Coordinate with internal teams, vendors, and application owners

• Carry integrations through completion rather than handing off unresolved dependencies

PR: $70/hr.

Skills and Requirements

What We’re Looking For We are looking for someone with strong experience building and operating enterprise data integrations. You should be comfortable with most of the following:

• Designing reliable data-ingestion architectures
• Working with modern cloud data platforms
• Integrating heterogeneous enterprise systems
• Batch, streaming, CDC, API, file, and database ingestion patterns
• Cloud identity, permissions, networking, and security concepts
• Data governance and access-control concepts
• Troubleshooting distributed systems across organizational boundaries
• Working independently with application owners and infrastructure teams
• Turning ambiguous integration requests into concrete technical plans Experience with Databricks and AWS is strongly preferred because those are core parts of our current environment.

Experience with technologies such as Kafka, object storage, SaaS platforms, enterprise databases, SFTP, REST APIs, or similar integration mechanisms is useful, but we do not expect candidates to have worked with every system in our environment.

What Success Looks Like A successful person in this role can take a request such as: “We need this enterprise system available in the Lakehouse.” …and independently determine what needs to happen across the source application, connectivity, authentication, security, ingestion architecture, Databricks configuration, historical loading, validation, and production operations.

The goal is not simply to write pipelines. The goal is to make data-source onboarding happen end to end.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal employment opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment without regard to race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request to [email protected].