Senior Data Platform Engineer

Responsibilities

• Own the technical implementation and operational support of Astronomer and Apache Airflow.
• Develop, maintain, test, and troubleshoot Airflow DAGs.
• Establish standards for DAG structure, naming, dependencies, retries, scheduling, alerting, and error handling.
• Build and maintain CI/CD pipelines for Airflow DAGs, plugins, Python packages, configuration, and supporting components.
• Automate deployments across development, QA, UAT, and production environments.
• Implement automated validation, unit testing, integration testing, and deployment checks for data pipelines.
• Manage Airflow connections, variables, secrets, pools, queues, executors, and environment configuration.
• Integrate Airflow with Azure Data Factory, Microsoft SQL Server, APIs, file systems, cloud storage, and other platforms.
• Develop and support Azure Data Factory pipelines, datasets, linked services, triggers, parameters, and integration runtimes.
• Orchestrate ADF pipelines from Airflow where appropriate.
• Develop and optimize SQL Server extraction, transformation, and loading processes.
• Troubleshoot SQL queries, stored procedures, connectivity, performance, and pipeline-related failures.
• Build reusable operators, hooks, sensors, libraries, and pipeline templates.
• Implement pipeline monitoring, logging, alerting, dashboards, and operational metrics.
• Improve pipeline reliability, recoverability, scalability, and performance.
• Define retry, restart, backfill, catch-up, and failure-recovery procedures.
• Implement secure secret management and eliminate credentials from code and configuration.
• Support network connectivity, identity, access control, certificates, and private endpoints with infrastructure and security teams.
• Maintain technical documentation, deployment procedures, support runbooks, and architectural diagrams.
• Mentor data engineers on Airflow, Python, testing, CI/CD, and production-support practices.
• Participate in evaluation and potential adoption of Databricks and modern data-platform technologies.

Requirements

• Five or more years in data engineering, platform engineering, DevOps, software engineering, or data-platform operations.
• Strong production experience with Apache Airflow and hands-on Astronomer or comparable managed Airflow.
• Strong Python and DAG development skills with CI/CD design and support.
• Experience deploying pipelines across multiple controlled environments and using Git-based workflows.
• Strong Azure Data Factory and Microsoft SQL Server experience with advanced SQL troubleshooting.
• Integration experience with databases, REST APIs, files, cloud storage, and enterprise applications.
• Automated testing for data pipelines and orchestration code with logging, monitoring, alerting, retry, and failure-recovery patterns.
• Diagnosis of complex production failures across application, orchestration, database, network, and infrastructure layers.
• Understanding of secrets management, identity, access control, and secure configuration.
• Airflow and Astronomer: DAG design, task groups, operators, hooks, sensors, callbacks, dynamic mapping, scheduling and timetables, connections and variables, retries and SLAs, backfills and recovery, plugins and shared packages, DAG testing, Astronomer deployments and CLI, containerized development, upgrades and dependency management, logging and metrics, performance troubleshooting, integration with ADF and SQL Server.
• Ci/CD practices: Python linting and formatting, DAG validation, unit and integration testing, SQL validation, dependency scanning, container image building, security scanning, environment configuration, artifact versioning, automated approvals, promotion across environments, rollback and recovery, audit history, release notes.
• SQL Server: complex SQL, stored procedures, views, functions, performance tuning, deadlocks, incremental loads, reconciliation queries, authentication and managed identity, collaboration with DBAs, resource efficiency for Airflow and ADF workloads.
• Azure Data Factory: pipeline development, datasets, linked services, triggers, parameters, integration runtimes, reusable patterns, self-hosted runtimes, troubleshooting, orchestration coordination, platform selection, monitoring and alerting, promotion across environments.
• Preferred: Azure DevOps Pipelines, GitHub Actions, GitLab CI, Jenkins, Docker, Kubernetes, Airflow on Kubernetes, Terraform, Azure Key Vault, managed identities and service principals, ADLS Gen2, Databricks, Spark, PySpark, Delta Lake, Kafka, dbt, VaultSpeed or Data Vault 2.0, OpenTelemetry, Azure Monitor, Grafana, Prometheus, and experience with manufacturing, ERP, finance, supply-chain, or operational data.

Six-month Success Measures

• Assess current state, document artifacts and risks, set standards, implement reliable CI/CD and testing, reduce manual deployments, improve observability, resolve recurring failures, document recovery procedures, improve secret management, build templates and libraries, train engineers, and propose future-state architecture.