Data Engineer

Position: Senior Data Engineer — Analytics Platform Engagement Mode: Not specified Location: Not specified Position Overview: We are building an analytics platform on top of a set of operational source systems and are seeking a Senior Data Engineer to architect and build the ingestion layer that pulls event and reference data from these systems into a clean, queryable analytics platform. This is a hands-on, autonomous role where, in the initial phase, you will act as the sole architect — choosing ingestion strategies for each source, designing the event schema and data model, and proving the pipeline end to end on one source before scaling to the rest. As the platform matures, you will play a key role in shaping the team that builds on top of it. The role demands strong technical ownership, pragmatic delivery, and the ability to work across complex, on-prem operational environments with segregated network zones. Key Responsibilities: • Design and build the ingestion layer from on-prem operational systems into Kafka using CDC (Debezium) off SQL Server backends and vendor REST/SOAP APIs where appropriate • Stand up the streaming backbone including Kafka, Kafka Connect, and Schema Registry (Avro/Protobuf) with schemas that survive dedup, replays, and out-of-order delivery • Orchestrate batch loads, backfills, dimension refreshes, and reconciliation jobs in Airflow • Model core domain entities and the event streams they generate with correct semantics for downstream analytics • Land data in the warehouse/lakehouse and build reconciliation checks that prove completeness and correctness • Own documentation, infrastructure-as-code, and runbooks from day one as a core deliverable • Work with the security/network team to move data out of segregated network zones safely • Architect ingestion strategies for each source system before scaling across the platform • Prove the pipeline end to end on one source before fanning out to additional sources • Shape and grow the data engineering team as the platform matures Required Skills: • 5+ years in data engineering with real ownership of an ingestion architecture end to end • Strong Kafka expertise beyond produce/consume: Kafka Connect, Schema Registry, and change data capture (Debezium or equivalent) • Deep SQL Server / T-SQL skills: reading undocumented schemas, enabling CDC, working against production databases without disruption • Airflow or comparable orchestrator for production batch pipelines 
 API/SDK integration experience with messy, proprietary, or poorly documented sources • Solid data modeling, especially event-heavy data with slowly-changing dimensions • Pragmatic delivery approach: ability to land one source cleanly before scaling • Experience with REST and SOAP API integrations • Avro and Protobuf schema design • Infrastructure-as-code practices • Experience designing and managing event schemas for deduplication, replays, and out-of-order delivery Preferred (Bonus) Skills: • Experience working in on-prem, secure, or air-gapped network environments (VPNs, jump hosts, hybrid architectures) • Familiarity with data privacy and compliance standards such as GDPR, data retention, and residency requirements for sensitive PII • Lakehouse and/or warehouse experience with platforms such as Snowflake, BigQuery, or Databricks