Alter Domus is hiring a Data Engineering Manager to lead the design, development, and maintenance of our data infrastructure and pipelines. You will oversee a team of data engineers, collaborating closely with data analysts, data scientists, and business stakeholders to ensure data is reliable, efficient, and accessible at scale.

What You'll Do

Lead, mentor, and develop a team of data engineers, fostering a culture of collaboration, innovation, and continuous improvement.
Set clear goals and performance expectations for team members, conducting regular performance reviews and providing constructive feedback.
Oversee the design, building, and maintenance of end-to-end data pipelines using Airflow (including managed services like Amazon MWAA) to orchestrate, schedule, and monitor batch/streaming workflows.
Ensure the implementation of DAGs (Directed Acyclic Graphs) with retry logic, error handling, and alerting to maintain data quality and pipeline reliability.
Guide the integration of data from various sources using Airbyte for ingestion and dbt for transformations in a scalable and modular fashion.
Collaborate with Data Analysts and Data Scientists to implement transformations and business logic, ensuring data is analytics-ready.
Direct the design and implementation of efficient data models for both structured and semi-structured data in AWS S3 (data lake) and Snowflake (data warehouse).
Utilize Databricks for data processing and analytics, enabling real-time insights and advanced analytics capabilities.
Ensure data schemas and transformations support advanced analytics, BI reporting, and machine learning use cases.
Lead the utilization of AWS Lake Formation APIs and best practices to maintain data security, access controls, and compliance.
Collaborate with IT security to establish robust encryption standards, audit trails, and identity/role-based access.
Oversee the optimization of AWS Athena queries and configurations (e.g., data partitioning) for performance and cost efficiency.
Monitor and tune Airflow DAGs, Snowflake queries, and Databricks jobs to improve throughput and reliability.
Partner with cross-functional teams, including DevOps, Platform Engineering, and Data Science, to ensure seamless integration of data workflows and systems.
Communicate technical solutions effectively to non-technical stakeholders and leadership, translating requirements into actionable tasks.
Participate in architecture reviews, code reviews, and troubleshooting sessions to ensure quality and alignment with best practices.
Stay current with emerging trends in data engineering, orchestration tools (Airflow, MWAA), and cloud services (AWS, Snowflake, Databricks).

What We're Looking For

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
5+ years of experience in data engineering or a similar role.
At least 2 years in a leadership or managerial capacity working with cloud-based data platforms.
Proficient in Airflow (self-managed or managed services like Amazon MWAA) for workflow orchestration, DAG development, and scheduling.
Familiarity with best practices for Airflow DAG structure, dependency management, and error handling.
Hands-on experience with AWS Lake Formation, S3, Athena, and related services (e.g., Lambda, Glue, IAM).