Role Description Accorded is seeking an experienced data engineer to join our team. The ideal candidate will have a strong background in data engineering, proficiency in SQL, Python, and modern data engineering tools, and an eagerness to learn our business domain. - Build and maintain data transformation pipelines with robust testing to ensure data integrity. - Design, implement, and maintain models with complex domain and business logic for Accorded’s Acumen product. - Collaborate with cross-functional teams (such as our Actuarial Team) to understand data requirements and deliver high-quality solutions. - Participate in code reviews and maintain high standards of code quality. Qualifications - 5+ years of engineering experience with data products. - Strong proficiency in SQL and Python. - Extensive experience in managing data transformation pipelines. - Solid understanding of data modeling, data warehousing, and ETL processes. - Experience with version control systems (e.g., Git). - Strong problem-solving skills and attention to detail. - Excellent communication skills and ability to work in a collaborative environment. Requirements - Experience with dbt (data build tool) is highly preferred. - Experience with tools like BigQuery and Snowflake. - Knowledge of healthcare data and related regulatory requirements (e.g., HIPAA). - Experience with dbt-flux or similar incremental materialization frameworks. Additional Information - While healthcare experience is not strictly required, a willingness and enthusiasm to learn about the healthcare domain is essential. - You will work closely with subject matter experts to understand domain-specific requirements and challenges. - Accorded is located in the San Francisco Bay Area. Remote candidates living and authorized to work in the continental United States are eligible to apply. - Accorded is an Equal Opportunity Employer. We are committed to creating a diverse and inclusive work environment and do not discriminate on the basis of race, color, religion, gender, gender identity, sexual orientation, national origin, genetics, disability, age, or veteran status.

Role Description This is a remote position. We are looking for a Junior/Middle Data Engineer with a strong focus on Python, SQL, and building data pipelines. The main responsibility of this role is connecting to various data sources, extracting data via REST APIs, databases, files, and third-party platforms, processing it, and loading it into a data warehouse for further analytics and BI reporting. We are looking for someone who understands that a data pipeline is not just a script, but a stable process with logging, error handling, retries, monitoring, and data quality checks. Location: Kazakhstan/Remote Qualifications - Strong knowledge of Python and practical experience using it in data engineering tasks. - Experience building data pipelines for loading, processing, and transforming data. - Experience working with various data sources: REST APIs, databases, CSV/Excel/JSON files, cloud storage, and third-party platforms. - Hands-on experience integrating with REST APIs: authentication, pagination, rate limits, retries, timeout handling, and error handling. - Understanding of how to build fault-tolerant pipelines. - Experience setting up incremental data loading and handling partial loads. - Ability to work with JSON and semi-structured data. - Strong SQL knowledge: JOINs, CTEs, aggregates, and window functions. - Experience loading data into databases or data warehouses such as PostgreSQL, BigQuery, Snowflake, Redshift, MS SQL, or similar systems. - Understanding of ETL/ELT approaches. - Experience with logging, monitoring, and basic troubleshooting of pipelines. - Experience working with Git. Requirements - Experience working with dbt: models, sources, tests, documentation, incremental models. - Experience with Spark / PySpark. - Experience using orchestration tools such as Airflow, Prefect, Dagster, or similar. - Experience implementing data quality checks: freshness, duplicates, completeness, consistency. - Experience working with cloud storage: AWS S3, Google Cloud Storage, Azure Blob Storage. - Experience with Docker. - Understanding of dimensional modelling principles: fact/dimension tables, star schema, data marts. - Experience optimizing SQL queries and pipelines. Benefits - A variety of projects: trust us, you won’t be bored. - A sane schedule: we focus on tasks, not hours—but showing up at noon every day isn’t exactly smiled upon. - A team that values expertise and humor: yes, we occasionally crack jokes about SQL—don’t worry if you don’t laugh right away. - Choose your adventure: Dive deep into a single, large-scale project or opt for a “discovery” mode, collaborating with multiple global clients across different domains. You can get hands-on with cutting-edge data stacks for anything from gaming and dating to skyscraper construction and nuclear energy. If variety is what you crave, you’ll find it here.

Role Description As a Data Engineer, you will be engaged from the first client conversation all the way through to delivery — gathering requirements, designing the solution, and seeing it through to completion. - Design, construct, install, test, and maintain highly scalable data management systems and robust data pipelines. - Ensure data quality, reliability, and accessibility for our AI/ML engineers and LLM applications, leveraging cloud platforms and modern data engineering practices, including workflow orchestration. - Design, build, and optimize scalable ETL/ELT data pipelines using Python and cloud-native tools (on AWS, Azure, or GCP). - Develop data models and schemas optimized for analytical and AI/ML workloads. - Implement data quality checks and monitoring frameworks. - Manage and administer data warehouses, data lakes, and databases (SQL/NoSQL). - Implement and manage workflow orchestration tools (e.g., Airflow, Prefect, Dagster) for scheduling and monitoring data pipelines. - Collaborate closely with AI/ML Engineers and LLM Engineers to understand their data requirements. - Ensure data security and compliance standards are met. - Optimize data storage and processing costs on Hyperscaler platforms. - Write efficient and maintainable Python code for data processing tasks. - Work independently to troubleshoot and resolve data-related issues. Qualifications - Strong proficiency in Python for data manipulation and pipeline development (e.g., Pandas, PySpark). - Expertise in SQL and experience with relational and NoSQL databases. - Hands-on experience with cloud-based data services on at least one Hyperscaler (e.g., AWS S3/Glue/Redshift, Azure Data Factory/Synapse, GCP Cloud Storage/Dataflow/BigQuery). - Experience building and managing data pipelines and ETL/ELT processes. - Familiarity with data warehousing concepts and data modeling. - Understanding of data quality principles. - Ability to work independently and take ownership of data infrastructure components. How to Apply - Share your philosophy on developing data infrastructure, the methodologies you utilize, and provide concrete examples of data systems you've built that have delivered tangible results. - Tell us why you are interested to join Suzega. What Happens Next - We will review applications. - If shortlisted, you'll participate in two virtual interviews with our Trusted Interviewers. - You will have to go through a coding interview. - We will aim to complete our selection process within two weeks and notify you of our decision. Benefits - Please review the Benefits & Perks section in our Team Member Handbook for comprehensive information. Location Work from anywhere in India.

Role Description This is a remote position. KATBOTZ is seeking an experienced Databricks SME to support enterprise-level data engineering and analytics initiatives. The ideal candidate will have deep expertise in the Databricks ecosystem, big data technologies, cloud platforms, and modern data architecture. The candidate will work closely with cross-functional teams to design, optimize, and implement scalable data solutions for global clients. - Lead the design, development, and optimization of Databricks-based data platforms and pipelines. - Provide technical expertise on Databricks architecture, performance tuning, and best practices. - Build scalable ETL/ELT workflows using Apache Spark and Databricks. - Collaborate with business stakeholders, architects, and engineering teams to gather requirements and deliver solutions. - Implement Delta Lake, data governance, and data quality frameworks. - Support migration of legacy data platforms to Databricks. - Optimize data processing workflows for performance and cost efficiency. - Mentor development teams on Databricks, Spark, and cloud-native solutions. - Troubleshoot production issues and provide timely resolutions. - Ensure adherence to security, compliance, and coding standards. Qualifications - 10+ years of experience in Data Engineering and Big Data technologies. - Strong hands-on expertise in Databricks and Apache Spark. - Experience with Python, PySpark, SQL, and Scala. - Expertise in Delta Lake, Unity Catalog, and Databricks Workflows. - Strong understanding of data warehousing and modern lakehouse architecture. - Experience with cloud platforms such as Microsoft Azure, Amazon AWS, or Google GCP. - Experience integrating Databricks with BI and reporting tools. - Knowledge of CI/CD pipelines and DevOps practices. - Strong analytical and problem-solving skills. - Excellent communication and stakeholder management abilities. Preferred Qualifications - Databricks Certified Professional certification. - Experience in real-time streaming using Kafka or Structured Streaming. - Exposure to AI/ML workflows within Databricks. - Prior experience working with global remote teams. Benefits - Competitive compensation package. - Opportunities for professional development and career advancement. - Flexible working conditions, with remote options available. - Dynamic and supportive work environment. Company Description KATBOTZ LLC is an Equal Opportunity Employer. We provide equal employment opportunities to all qualified individuals, regardless of race, religion, gender, gender identity, age, marital status, national origin, sexual orientation, citizenship status, veteran status, disability, or any other legally protected status. As an organization, we are unwavering in our commitment to maintaining a discrimination-free work environment, and fostering a culture of inclusivity, belonging and equal opportunity for all employees and applicants.