Data Engineer at Keyrus responsible for building and optimizing data pipelines for major projects. Contributing to data solutions and ensuring data quality in a growing team.
About the role
- Databricks Architect leading enterprise data platform implementations at Allata. Blending architectural responsibilities with technical leadership in data products and pipelines.
Responsibilities
- Define the overall data platform architecture (Lakehouse/EDW), including reference patterns (Medallion, Lambda, Kappa), technology selection, and integration blueprint.
- Design conceptual, logical, and physical data models to support multi-tenant and vertical-specific data products; standardize logical layers (ingest/raw, staged/curated, serving).
- Establish data governance, metadata, cataloging (e.g., Unity Catalog), lineage, data contracts, and classification practices to support analytics and ML use cases.
- Define security and compliance controls: access management (RBAC/IAM), data masking, encryption (in transit/at rest), network segmentation, and audit policies.
- Architect scalability, high availability, disaster recovery (RPO/RTO), and capacity & cost management strategies for cloud and hybrid deployments.
- Lead selection and integration of platform components (Databricks, Delta Lake, Delta Live Tables, Fivetran, Azure Data Factory / Data Fabric, orchestration, monitoring/observability).
- Design and enforce CI/CD patterns for data artifacts (notebooks, packages, infra-as-code), including testing, automated deployments and rollback strategies.
- Define ingestion patterns (batch & streaming), file compacting/compaction strategies, partitioning schemes, and storage layout to optimize IO and costs.
- Specify observability practices: metrics, SLAs, health dashboards, structured logging, tracing, and alerting for pipelines and jobs.
- Act as technical authority and mentor for Data Engineering teams; perform architecture and code reviews for critical components.
- Collaborate with stakeholders (Data Product Owners, Security, Infrastructure, BI, ML) to translate business requirements into technical solutions and roadmap.
- Design, develop, test, and deploy processing modules using Spark (PySpark/Scala), Spark SQL, and database stored procedures where applicable.
- Build and optimize data pipelines on Databricks and complementary engines (SQL Server, Azure SQL, AWS RDS/Aurora, PostgreSQL, Oracle).
- Implement DevOps practices: infra-as-code, CI/CD pipelines (ingestion, transformation, tests, deployment), automated testing and version control.
- Troubleshoot and resolve complex data quality, performance, and availability issues; recommend and implement continuous improvements.
Requirements
- Previous experience as architect or lead technical role on enterprise data platforms.
- Hands-on experience with Databricks technologies (Delta Lake, Unity Catalog, Delta Live Tables, Auto Loader, Structured Streaming).
- Strong expertise in Spark (PySpark and/or Scala), Spark SQL and distributed job optimization.
- Solid background in data warehouse and lakehouse design; practical familiarity with Medallion/Lambda/Kappa patterns.
- Experience integrating SaaS/ETL/connectors (e.g., Fivetran), orchestration platforms (Airflow, Azure Data Factory, Data Fabric) and ELT/ETL tooling.
- Experience with relational and hybrid databases: MS SQL Server, PostgreSQL, Oracle, Azure SQL, AWS RDS/Aurora or equivalents.
- Proficiency in CI/CD for data pipelines (Azure DevOps, GitHub Actions, Jenkins, or similar) and packaging/deployment of artifacts (.whl, containers).
- Experience with batch and streaming processing, file compaction, partitioning strategies and storage tuning.
- Good understanding of cloud security, IAM/RBAC, encryption, VPC/VNet concepts, and cloud networking.
- Familiarity with observability and monitoring tools (Prometheus, Grafana, Datadog, native cloud monitoring, or equivalent).
Benefits
- At Allata, we value differences.
- Allata is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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