Engineering - Core Engineer

The role: Senior hands-on engineer to own a data and event backbone — how events flow, and how live and historical data are separated, stored, secured, and served. Accountable for delivering their scope, built correctly, securely, and on time. A hands-on expert engagement: building, not advising, requiring real streaming and data-modeling depth. This is core software development and transaction-processing work — building the operational data backbone of a live system. It is not an analytics, data-warehousing, or business-intelligence role; the data modeling here is transactional (OLTP-style), not dimensional / reporting modeling. Must-have summary — the hard bar A candidate must clear all of these to be a fit:? 6–8 years hands-on, currently building — recent, personally-built and delivered work they can speak to in depth.? Streaming / event-processing depth — partitioning, ordering, consumer semantics, replay (not batch-ETL-only).? Strong SQL and relational data modeling / projection design — the read layer is relational; SQL-light is not a fit.? Object storage and lakehouse depth, including its security posture — not just basic file-store usage.? Streaming-to-object-store integration — moving the event log to archival storage with idempotent / exactly-once delivery.? Data-plane security — tenant / row-level isolation, object-storage security (encryption, immutability, public-access blocking), and encryption in transit.? Test judgment for data properties — event replay, projection-versus-log correctness, idempotency, cross-tenant isolation.? AI-assisted-development fluency and verification — directs and verifies AI-generated code and tests, catching plausible-but-wrong output.? Strong Python; serverless-first, container fluency, local cloud emulation.? Ownership and delivery discipline; clear communication; can hold core consistency under direction when the lead is unavailable. Core skills — full detail? 6–8 years hands-on, with recent work you personally built and delivered.? Depth in streaming / event-processing platforms — partitioning, ordering, consumer semantics, replay.? Streaming-to-object-store integration — moving the event log into archival object / lakehouse storage reliably, with idempotent / exactly-once delivery and schema handling.? Strong relational database proficiency — the read / projection layer is relational and central to the role.? Strong read-model / projection design and data modeling.? Object storage and lakehouse depth — bucket / prefix design, partitioning, schema evolution, snapshot / lifecycle management, and columnar query over it.? Data-plane security across all three stores:? Tenant / party data isolation — row / tenant-level isolation, enforced server-side and per request.? Object-storage security — public-access blocking, bucket / prefix isolation, encryption with managed keys, immutability where retention requires it, and lifecycle / tiering.? Encryption at rest and in transit across the relational store, the event log, and object / lakehouse storage.? Sound data-lifecycle judgment — what to cache, project, or archive, and why; hot vs. cold separation.? Security verification of AI-generated code — catches data-exposure and access-control flaws in generated output before they land.? Testing the hard data-plane properties — event ordering / replay, projection-versus-log correctness, idempotency / exactly-once, and cross-tenant isolation (proving data can't leak).? Test strategy and verification judgment — reviews generated tests for genuine coverage rather than green-but-hollow passing.? Hands-on with automated testing frameworks — unit / integration testing, service mocking / stubbing, and test-data generation, alongside local cloud emulation.? Ownership and delivery discipline — accountable for getting work to done under time pressure.? Clear communicator — surfaces risk and status clearly.? Strong Python proficiency.? Serverless-first cloud-native build — object storage and serverless compute as primary building blocks.? Container fluency — containerized local development and container-image packaging of compute.? Local cloud emulation for development and testing.? Fluent with modern AI-assisted development tooling — directs and verifies AI-generated code with rigor.? Comfortable applying an established architectural decision framework under direction — able to hold core consistency when the lead is unavailable. Advantageous? Cloud data services.? Key management / secrets handling for data stores.? Data retention / records-lifecycle and immutability experience.? Container orchestration — good to know, not required.? High-volume IoT / telemetry data.? Observability / distributed-tracing tooling.? Domain exposure in a data-intensive, operationally complex industry.

Assessment: A deep-dive on a data / event system you personally built — including how you moved the event stream into archival storage, isolated tenants, secured object storage, and protected data at rest and in transit — plus how you'd verify a data-layer implementation is correct and judge whether its test suite proves the hard properties (replay, projection correctness, isolation).EEO:"Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans."