Data Engineer

Nelnet Servicing•Lincoln, NE

9d•$100,000•Hybrid

About The Position

Nelnet Business Services (NBS), a division of Nelnet, Inc., provides payment technology and education services to more than 1,200 higher education institutions, nearly 12,000 K-12 schools, and millions of individual students, families, and supporters across the globe. Our culture of service enables us to form long-lasting and trusted partnerships, while our focus on creativity and innovative solutions empowers our customer communities to thrive. The Data Engineer builds and maintains the ingestion pipelines, change-data-capture (CDC) streams, and Bronze-layer infrastructure that bring source-system data into Snowflake reliably, on time, and at the right grain. The Data Engineer ensures data is complete, ordered, and historically preserved — creating the raw material the Analytics Engineer models into governed data products. This is more than an ingestion-only role, and it is built to grow. To start, the Data Engineer owns reliable ingestion and the Bronze layer, and works closely with the Analytics Engineer on the mechanics of getting data into conformed Silver models — including the reconciliation that proves the data ties out end to end. Over time, with Analytics Engineer partnership and Architect support, the Data Engineer develops deeper ownership of conforming work: turning messy, multi-system source data into models that mean the same thing across every source. This is an early hire on a small, capability-based data squad, with a clear growth path, working on the foundational data products of a new data platform.

Requirements

Experience building and operating production data pipelines with strong Python and SQL skills.
Solid working knowledge of relational databases (SQL Server, Oracle, or PostgreSQL).
Experience implementing data-quality checks at the ingestion boundary — schema-drift detection, row-count validation, freshness monitoring — and reconciling that data ties out end to end.
General familiarity with data modeling concepts.
Strong analytical and problem-solving skills, with the discipline to prove data is correct rather than assume it.
Comfort working closely with an Analytics Engineer — taking direction on modeling logic while owning the build and reliability of it.
A genuine interest in growing into conforming and Silver-layer modeling work; coachable and motivated by a defined growth path.
Clear written and verbal communication, including documenting pipelines and surfacing issues early rather than absorbing them silently.
Attention to detail with data quality, historical preservation, and auditability.
Ability to manage competing priorities and participate in incident response with composure.
Collaborative working style across engineering, platform, governance, and product roles.

Nice To Haves

Conforming data across systems — reconciling differing keys, codes, and grains from multiple sources into a single consistent model.
Deeper ownership of orchestration (Dagster) — scheduling, retries, failure alerting, SLA monitoring.
The modern streaming / CDC stack — Redpanda for change-data-capture.
Governance-integrated engineering — classification tagging and the promotion gates that depend on it, in partnership with Data Governance.
dbt-based layered modeling (Bronze / Silver / Gold) at production maturity.
Any prior exposure to CDC / streaming platforms (Redpanda, Kafka, or similar).
Experience with a document / NoSQL platform (MongoDB preferred).
Experience with Snowflake (multi-tenant patterns, Snowpipe, tasks, streams).
Containerization and provisioning experience with Docker.
Experience consolidating single-tenant sources into a multi-tenant warehouse.
Exposure to data-governance practices — classification, masking, retention — in a regulated data environment.
Experience with financial, payment, or transaction data, including reconciliation and settlement concepts.

Responsibilities

Build and maintain CDC and batch pipelines from source systems into the Snowflake Bronze layer using Redpanda, Python, and Dagster.
Manage source-specific adapters across relational (SQL Server, Oracle), document (MongoDB), API, and file-based sources, and stand up new sources as the platform grows.
Handle the complexity of consolidating single-tenant source databases into a multi-tenant Snowflake model.
Implement idempotent load patterns with deduplication so pipelines can re-run safely without producing duplicate or missing records.
Design and maintain raw snapshot tables that preserve source-system state with minimal transformation — the immutable audit trail back to source.
Implement historical tracking so no data is silently overwritten.
Handle hard deletes from source systems by tracking deletions in Snowflake rather than losing them.
Maintain source-schema documentation and change detection — alert when upstream schemas drift unexpectedly.
Build conformed Silver models from the business rules, grain, and conforming logic the Analytics Engineer defines — starting with close guidance and taking on more independence over time.
Own Bronze-to-Silver reconciliation: row counts and totals tie out, no records lost or double-counted across the transformation.
Help build and automate the mechanical contract gates — reconciliation tie-out, freshness, and schema validation — as CI/CD checks.
Apply classification tags at Bronze so columns can promote to Silver, partnering with the data owner and Data Governance on classification decisions.
Handle incremental logic and performance tuning as models grow in volume, with support as needed.
Run and monitor Dagster orchestration — scheduling, retries, failure alerting — and grow into fuller ownership of it, including SLA monitoring, over time.
Implement data-quality checks at the ingestion boundary (row counts, schema validation, freshness checks).
Monitor Snowflake concurrent-write limits and tune ingestion patterns to avoid bottlenecks.
Participate in severity-1 incident response — failed file loads, latency cascades, duplicate transactions, and similar.
Work with Platform Engineering on Docker-based containerization and provisioning of Snowflake databases, schemas, and warehouses.
Apply required tags (domain, squad, product, layer, environment) to Snowflake objects, Dagster jobs, and streaming topics for cost attribution.
Participate in the HVR-to-Redpanda CDC migration and validate data integrity during cutover.
Contribute to shared CI/CD pipeline templates that standardize ingestion and build patterns.