Databricks Engineer (Tax & Audit)

Improving · Remote

Full time Data Science Today

Required skills & experience

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.

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 clarify requirements, translate analytics needs into technical designs, and ensure outputs meet accuracy and timeliness expectations.
Own Databricks workflows end-to-end—from development through deployment and ongoing support.
Optionally contribute to Databricks DevOps practices (nice to have), improving CI/CD, environment management, and release processes.

Goals: deliver dependable data products for Tax and Audit workflows, improve pipeline performance and maintainability, and ensure high-quality results through testing and careful attention to detail.

Nice to have

Familiarity with Databricks DevOps practices (e.g., CI/CD concepts, environment/version management, and deployment workflows).
Experience contributing to data governance patterns such as dataset documentation, consistency checks, and maintainable conventions across teams.
Prior exposure to testing strategies beyond unit tests (e.g., integration tests for pipelines) to further improve reliability.

Benefits

Comprehensive medical, dental, vision, and life insurance
Mental health support
Savings Fund & Corporate Retirement Plan
Paid time off, vacation bonus, and Christmas bonus
Career development, certifications, and continuous learning
TotalPass wellness program
Collaborative culture with internal events and technical communities
Opportunity to work on innovative global projects with cutting-edge technologies

Big DataDatabricksPython

Source: GetOnBoard

Report this job