{"id":3766131,"url":"https://alion.io/job/tala-senior-data-engineer","title":"Senior Data Engineer","company":{"id":1796572,"name":"Tala","domain":"tala.co","url":"https://alion.io/company/tala-co","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":130000,"max":200000,"currency":"USD","period":"year","gross":null,"usd_annual":200000},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Hudi","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Blue-Green Deployment","optional":false},{"name":"CI/CD","optional":false},{"name":"Delta Lake","optional":false},{"name":"Helm","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"Terraform","optional":false},{"name":"Terragrunt","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"AWS Glue","optional":true},{"name":"Flink","optional":true},{"name":"Presto","optional":true},{"name":"Trino","optional":true}],"status":"live","first_seen_at":"2026-11-04T19:21:14Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-06T06:44:18Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"The Role\nWe are looking for a Senior Data Engineer to help build, operate, and continuously improve the data infrastructure that powers analytics and business intelligence across Tala's global markets.\nIn this individual contributor role, you will take ownership of Tala's data synchronization pipelines, ensuring that data from our market-level databases is reliably captured, processed, and delivered to Snowflake for downstream analytics and business use.\nOur data infrastructure uses a modern AWS-based architecture, including AWS Database Migration Service (DMS), Kafka, Spark Structured Streaming, Apache Iceberg, Amazon S3, and Snowflake. You will work across this ecosystem to ensure data is accurate, available, and delivered with minimal latency.\nBeyond maintaining existing pipelines, you will identify opportunities to improve performance, scalability, reliability, and cost efficiency. You will also establish and monitor service level objectives (SLOs), strengthen testing and deployment practices, and lead root cause analysis when issues arise.\nThis is an opportunity for a hands-on engineer who enjoys solving complex distributed data challenges and takes ownership of production systems from development through operation.\nWhat You'll Do\nOwn and operate data pipelines: Maintain and improve Tala's change data capture (CDC) and data synchronization infrastructure, ensuring accurate and timely delivery of data from market-level databases into Snowflake.\n\nImprove pipeline performance and scalability: Identify and implement improvements to reduce latency, optimize infrastructure costs, and support growing data volumes across markets.\n\nBuild and evolve data infrastructure: Onboard new data sources, tables, and markets while managing schema evolution and ensuring compatibility across systems.\n\nDrive reliability and data quality: Establish service level objectives (SLOs), implement monitoring and alerting, and build automated data quality checks to ensure data remains accurate and available.\n\nStrengthen engineering practices: Develop and maintain unit and integration tests, CI/CD workflows, infrastructure-as-code, and safe deployment strategies, including dry runs and blue-green deployments.\n\nLead incident resolution: Troubleshoot production issues, conduct root cause analyses, and translate lessons learned into better monitoring, testing, documentation, and operational runbooks.\n\nContribute to technical direction: Participate in architecture and design discussions, write and review technical design documents, and provide thoughtful feedback through code reviews.\n\nCollaborate across teams: Partner with Data Science, Analytics, Data Platform, Product, Credit, and Business Development teams to understand their data needs and ensure reliable access to the information they depend on.\n\nWhat You'll Need\n4+ years of experience in backend engineering or data engineering, ideally supporting consumer-scale applications.\n\nAt least 2 years of hands-on professional Python experience.\n\nProven experience independently building, operating, and improving production-grade data pipelines.\n\nHands-on experience with change data capture (CDC) and stream processing technologies such as Kafka, Amazon MSK, AWS DMS, or comparable tools.\n\nStrong proficiency in Apache Spark, particularly Spark Structured Streaming.\n\nExperience with modern data lakehouse architectures and table formats such as Apache Iceberg, Delta Lake, or Apache Hudi, as well as Parquet.\n\nStrong database experience across relational and analytical systems, including MySQL, PostgreSQL, Amazon RDS, and Snowflake.\n\nHands-on experience with AWS cloud infrastructure, ideally including EMR, S3, MSK, DMS, RDS, and EKS.\n\nExperience with infrastructure-as-code and CI/CD tools such as Terraform, Terragrunt, Helm, and Jenkins.\n\nFamiliarity with pipeline orchestration and job scheduling tools such as Airflow or Python-based services running on Kubernetes.\n\nStrong understanding of distributed systems, scalable algorithms, and large-scale data processing.\n\nAbility to communicate complex technical concepts clearly and collaborate effectively with engineering teams and business stakeholders.\n\nNice to Have\nExperience with Trino or Presto.\n\nExperience with Kubernetes and containerized applications.\n\nFamiliarity with AWS Glue.\n\nExperience with additional stream processing technologies such as Debezium, Amazon Kinesis, or Apache Flink.\n\nExperience with other modern data lakehouse technologies such as Delta Lake or Apache Hudi.\n\nAdvanced degree in Computer Science, Mathematics, or a related technical 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