[C3F] Data Platform Engineer

Software Mind

6d•Remote

About The Position

Software Mind develops solutions that make an impact for companies around the globe. Tech giants & unicorns, transformative projects, emerging technologies and limitless opportunities – these are a few words that describe an average day for us. Building cross-functional engineering teams that take ownership and crave more means we’re always on the lookout for talented people who bring passion and creativity to every project. Our culture embraces openness, acts with respect, shows grit & guts and combines employment with enjoyment. Project – the aim you’ll have Our customer provides innovative solutions and insights that enable our clients to manage risk and hire the best talent. Their advanced global technology platform supports fully scalable, configurable screening programs that meet the unique needs of over 33,000 clients worldwide. Headquartered in Atlanta, GA, they have an internationally distributed workforce spanning 19 countries with about 5,500 employees. Our partner perform over 93 million screens annually in over 200 countries and territories. Role – how you’ll contribute Build and operate change-data-capture pipelines from PostgreSQL into Azure, using Kafka Connect and Debezium as the core of the platform Configure, deploy and scale connectors end to end - connector setup, task management, offsets, schema history, and snapshot strategy Run these pipelines as stateful workloads on Kubernetes (AKS), covering configuration, secrets, networking and resource tuning Monitor and troubleshoot the platform in production: connector failures, task rebalances, restarts, throughput and backpressure, message-size limits, retries and recovery Automate the platform in Python - configuration-driven onboarding of new data sources, pipeline orchestration, monitoring and alerting, recovery workflows, and automated testing Integrate CDC streams with the wider Azure data stack: Event Hubs, ADLS, Azure PostgreSQL, ADF and Databricks Manage platform infrastructure as code, so environments are reproducible and changes are reviewable Apply data protection requirements to sensitive data flowing through the pipelines - masking, hashing, access control and retention

Requirements

Solid commercial experience as a data or platform engineer, with hands-on work on streaming or CDC pipelines rather than batch reporting alone
Practical Kafka knowledge - topics, partitions, offsets, consumer groups and delivery semantics - including at least one Kafka Connect deployment you ran yourself
Strong SQL and PostgreSQL skills, with working knowledge of WAL, logical replication, replication slots and replication lag
Working understanding of CDC concepts: initial snapshots, inserts, updates and deletes, event ordering, at-least-once delivery, and schema evolution
Confident Python for automation and tooling - orchestration, monitoring, recovery scripts, and automated tests
Hands-on experience with Azure data services, for example Event Hubs, ADLS or Azure PostgreSQL
Comfortable working with Kubernetes as a user: deploying workloads, handling configuration and secrets, reading logs, debugging failing pods
Ability to debug a running pipeline from metrics and logs - telling throughput problems from backpressure, retries or a genuine connector failure

Nice To Haves

Production experience with Debezium specifically - snapshot strategies on large tables, schema history recovery, offset loss, and bringing connectors back after failure
Experience operating stateful workloads on AKS: StatefulSets, stable worker identity, and resource tuning under load
Infrastructure-as-code and CI/CD for data platform components (Terraform, Bicep or similar)
Hands-on work with Databricks and ADF at production scale
Experience implementing data protection controls for sensitive data - masking, hashing, access control and retention policies

Responsibilities

Build and operate change-data-capture pipelines from PostgreSQL into Azure, using Kafka Connect and Debezium as the core of the platform
Configure, deploy and scale connectors end to end - connector setup, task management, offsets, schema history, and snapshot strategy
Run these pipelines as stateful workloads on Kubernetes (AKS), covering configuration, secrets, networking and resource tuning
Monitor and troubleshoot the platform in production: connector failures, task rebalances, restarts, throughput and backpressure, message-size limits, retries and recovery
Automate the platform in Python - configuration-driven onboarding of new data sources, pipeline orchestration, monitoring and alerting, recovery workflows, and automated testing
Integrate CDC streams with the wider Azure data stack: Event Hubs, ADLS, Azure PostgreSQL, ADF and Databricks
Manage platform infrastructure as code, so environments are reproducible and changes are reviewable
Apply data protection requirements to sensitive data flowing through the pipelines - masking, hashing, access control and retention