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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** WORK ANYWHERE NOW LLC - **Location:** Indianapolis, IN, United States (Remote available) - **Experience:** Expert - **Contract:** Contract - **Skills:** Airflow, Amazon Web Services, Microsoft Azure, BigQuery, Code Review, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Distributed Computing Environment, Distributed Data Store, Monitoring of Systems, Python (Programming Language), Machine Learning, DataOps, Software Engineering, SQL Databases, Cloud Platform System, Snowflake, Apache Spark, Data Management, Machine Learning Operations, Software Version Control, Data Pipelines, Databricks - **Published:** September 29, 2026 - **Apply:** https://www.careerjet.com/jobad/us65f3bda0c203e9e57c3adbc0be808681 ## About the Role * 5+ years of experience in data engineering, data platform engineering, or data-intensive software engineering. * Advanced professional experience with Python and SQL. * Strong experience designing and owning production-grade distributed data architectures. * Solid understanding of ETL/ELT patterns, data modeling, orchestration, and data observability. * Strong software engineering fundamentals, including testing, CI/CD, version control, and system design. * Experience handling large, complex, and heterogeneous datasets in cloud environments. * Ability to work independently, manage technical ambiguity, and take ownership of deliverables. Bonus points * Experience with cloud platforms (AWS, GCP, or Azure) and technologies like Spark, Databricks, Snowflake, or BigQuery. * Experience with workflow orchestrators such as Apache Airflow, Dagster, or Prefect. * Domain experience in life sciences, clinical trials, genomics, or drug discovery datasets. * Exposure to data governance, lineage, or operating within regulated data environments. ## Description As a Senior Data Engineer, you will own the end-to-end architecture, design, and implementation of scalable data platforms and processing pipelines. You will work directly with scientists, researchers, and machine learning engineers to translate complex domain requirements into technical solutions that power downstream analytics and production ML systems. We are looking for a senior technical owner who can operate autonomously, drive best practices across distributed processing and data modeling, and collaborate effectively with international stakeholders in an English-speaking environment. Selection process The process usually consists of 4 stages and takes on average 4 weeks to be completed, depending on eventual additional technical rounds or take-home tests. * Initial interview with Workana's recruiting team; * Technical interview with Workana's technical team; * Client Cultural fit interview; * Final interview with C-level. As Workana has multiple clients, if you pass the first round with Workana's recruiting team, you may also be considered for other relevant opportunities if the initial opportunity does not move forward. Responsibilities * Architect, build, and maintain production-grade data platforms and scalable ELT/ETL pipelines. * Ingest, transform, and model complex structured and unstructured scientific and clinical datasets. * Define data architecture patterns, engineering standards, and best practices across the team. * Collaborate with scientists, ML engineers, and business stakeholders to turn domain needs into data solutions. * Design data infrastructure that supports machine learning training, inference, and analytics workloads. * Ensure data quality, lineage, reproducibility, security, and system observability. * Optimize pipeline performance, architectural bottlenecks, and infrastructure cost efficiency. * Participate in technical design discussions, code reviews, and architectural decision-making. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Making Data Warehouses fast. 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