Databricks Engineer (Tax & Audit)
Worldwide | Sept. 2, 2026
Report as Closed
Company: Improving
Country: Worldwide
Type: Remote
Employment: Full-time
Description: Improving is an IT services firm focused on AI, Data, and Applications. We modernize legacy systems, build cloud-native platforms, and deliver future-ready solutions through collaborative, long-term partnerships that drive measurable outcomes.
At Improving South America, we provide IT services to transform the perception of the IT professional. We focus on IT consulting, software development and agile training.
The company promotes an exceptional work culture based on teamwork, excellence and fun, with a focus on personal growth and shared rewards. By joining, the candidate will become part of a community that prioritizes open communication and strong long-term working relationships, supported by a structure of professional development and continuous learning.
We need someone with proven, hands-on software development experience and strong data engineering depth for Databricks-based delivery.
- 5+ years of hands-on software development experience.
- Strong expertise in Apache Spark, with a proven record of delivering on Databricks.
- Proficient in Python with advanced data engineering skills using PySpark, Pandas, and related libraries.
- Experience developing and maintaining unit tests for Databricks workloads.
- Solid understanding of columnar storage (e.g., Parquet).
- Hands-on experience with Delta Lake / Delta Tables.
- Ability to handle small to large data volumes, including performance tuning and optimizations.
- Demonstrated ability to build data ingestion, transformation, and analytics pipelines.
- Strong analytical and problem-solving skills with close attention to detail.
- Comfort working with Databricks workflows and delivering outcomes reliably.
How we work (traits we value):
- Quality mindset: careful validation, correctness, and attention to details—especially important for Tax and Audit contexts.
- Ownership: you take responsibility for pipeline stability, performance, and maintainability.
- Communication: we expect clear collaboration with technical and non-technical stakeholders to align data outputs with business needs.
- Continuous improvement: you actively look for ways to simplify pipelines, improve performance, and strengthen testing coverage.
We are Improving, an IT services firm focused on AI, Data, and Applications. We modernize legacy systems, build cloud-native platforms, and deliver future-ready solutions through collaborative, long-term partnerships that drive measurable outcomes. In this role, we are looking for Databricks Engineers to support the Tax and Audit group. You will help design, build, and operate data ingestion, transformation, and analytics pipelines that enable reliable reporting and downstream consumption. The work centers on turning raw data into structured, governed datasets—leveraging Apache Spark, Databricks, Delta Lake/Delta Tables, and columnar formats—so business stakeholders can trust the outputs used for audit and tax-related analytics.
Responsibilities
We are looking for Databricks Engineers to join our Tax and Audit group and strengthen the team’s delivery across the data lifecycle.
- Build and maintain Apache Spark solutions delivered on Databricks, including scalable transformations and production-ready workloads.
- Develop and optimize data ingestion, transformation, and analytics pipelines that handle small to large data volumes.
- Use advanced Python and PySpark to implement efficient data engineering patterns (e.g., batch processing, enrichment, and data quality steps).
- Apply strong understanding of columnar storage (e.g., Parquet) and deliver production data sets using Delta Lake / Delta Tables.
- Write and maintain unit tests for Databricks workloads to improve reliability and reduce regressions.
- Perform performance tuning and optimization (Spark execution efficiency, partitioning strategies, and query/read/write improvements).
- Collaborate with stakeholders to requirements, translate analytics needs into technical designs, and ensure outputs meet accuracy and timeliness expectations.
- Own Databricks workflows end-to-end—from development through dep
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