Data Platform Engineer

Company Overview
At Bridgenext, we engineer Growth Operating Systems. Most enterprises have spent millions on their revenue stack and still aren't seeing the growth they expected.They have tools that function, but no system that wins. We help growth-hungry companies close that gap by turning fragmented platforms, siloed teams, and disconnected data into one integrated Growth OS. More than a technology company or marketing agency, we're a global digital consultancy with experts in engineering, data, AI, creative and more.
Our teams are made up of experts who believe in engineering impact, starting with putting people at the center of everything we do. Every team member directly shapes our work, culture, and values. Nothing matters more to us than a kind, respectful, fulfilling environment that supports everyone. Our flexible, inclusive culture gives you the autonomy, resources, and opportunities to thrive.
Position Description
We are looking for DataOps engineer with a hands-on experience to automate our Databricks Jobs/Workflows, DLT, and SQL tasks , implement and operate Unity Catalog at scale.
Key Responsibilities
Automation & Orchestration (Jobs/Workflows & CI/CD)
Design, build, and maintain Databricks Workflows/Jobs (multi‑task graphs, retries, triggers, alerts) for batch, and SQL workloads.
Implement CI/CD pipelines (GitHub Actions/Azure DevOps/GitLab): plan/apply for IaC, validate job JSON, environment promotion, approvals, and rollback.
Build templates, parameterized job definitions (ingestion/batch/streaming/SQL) with standardized logging, notifications, and governance.
Enforce run‑as service principals, job versioning, change auditability, and artifact immutability.
Automate provisioning via Terraform (Databricks provider), Databricks SDK (Python/Go), and REST API 2.1 for Jobs, DLT, Repos, SQL Warehouses, and Unity Catalog.
Unity Catalog – Automation, Governance & Migration
Catalog & Schema Provisioning (Automation‑first): Idempotent creation of catalogs, schemas, default locations, owners, grants, volumes, storage credentials, and external locations using Terraform/SDK/REST.
Grant‑as‑Code: Standardize RBAC with templates (OWNERSHIP, USE CATALOG/SCHEMA, SELECT, MODIFY, CREATE, READ/WRITE FILES); integrate SCIM groups and service principals.
Security & Audit: Dynamic views for row/column‑level security, data classification tags, audit logging, token policies, periodic permissions drift detection & remediation.
Delta Sharing: Automate providers/recipients, share objects, and scheduled access reviews/expiry.
Unity Catalog Migration (HMS → UC or multi‑workspace to UC): discovery, wave planning, external/managed table registration, permission model migration, dual‑run validation (row counts, schema parity, query replay), cutover & rollback.
Reliability & Data Quality
Define SLOs for pipeline success, latency, and data freshness; drive ≥99% T1 success and low MTTR.
Implement data quality checks (DLT expectations, dbt‑expectations/GX), idempotency, schema evolution strategy, checkpointing & replay.
Build incident response playbooks; lead RCAs and preventative actions.
Must Have Skills:
5–8 years of experience in data engineering, platform engineering, or cloud infrastructure roles.
Strong hands-on experience with Databricks administration (workspace management, cluster policies, access controls, cost optimization).
Working knowledge of Databricks workflow development (job orchestration, scheduling, pipeline monitoring).
Proficiency in Terraform for infrastructure as code and automated provisioning.
Solid experience with AWS cloud services and architecture.
Strong Python scripting skills for automation and tooling.
Good understanding of CI/CD practices and version control (Git).
Strong problem-solving skills and ability to work in a collaborative, fast-paced environment.
Exposure to or experience implementing Agentic AI solutions.
Bridgenext is an Equal Opportunity Employer

Work arrangement
No