Job description

Job Title: Data Engineer Location: Remote (India) Development Center: Ahmedabad, Gujarat, India Headquarters: Dallas, USA Experience: 2+ years Role Summary We are looking for a Data Engineer with strong SQL and programming skills who can design,build, and optimize scalable data pipelines and platforms — and who genuinely understands thedata they move. Beyond writing code, you should be able to read and reason about an existing codebase, identify root causes rather than symptoms, and connect engineering decisions back to what the business and the client are actually trying to achieve. You will collaborate with cross-functional teams to deliver reliable, high-quality data solutions while keeping performance, governance, and operational excellence in view. What You’ll Do • Design, build, and maintain robust batch and streaming data pipelines from ingestion through transformation to consumption. • Write clean, performant SQL and code (Python or Scala/Java) that others can read, test,and extend. • Model data thoughtfully — dimensional models, star/snowflake schemas, and the right warehouse/lake/lakehouse pattern for the problem. • Own pipelines end-to-end: monitoring, data quality, observability, lineage, and cost. • Read and analyze existing pipelines and code to debug issues and improve reliability and performance at scale. • Partner with analysts, data scientists, and business stakeholders to turn ambiguous requirements into dependable data products. Core Technical Skills • SQL — SQL proficiency (non-negotiable): window functions, query optimization — not just basic SELECTs. • Programming — At least one strong programming language: Python or Scala/Java. • Pipelines — ETL/ELT tooling such as Airflow, dbt, Dagster, or Luigi. • Distributed processing — Distributed processing with Spark or Flink (Hadoop acceptable for legacy contexts). • Cloud — Cloud platforms (AWS, GCP, or Azure) and their data services — Redshift,BigQuery, Snowflake, or Databricks. • Formats — File formats (Parquet, Avro, ORC) and a clear understanding of when andwhy each matters. Engineering Fundamentals (what separates good from average) • Version control (Git) and solid CI/CD practices. • Code quality: testing, modularity, and documentation. • Infrastructure-as-code (Terraform) and containerization (Docker, Kubernetes). • Working knowledge of data governance, lineage, privacy, and compliance. Signals We Look For • Can explain why a tool was chosen, not just that it was used. • Evidence of handling scale — data volumes, latency requirements, cost optimization. • Ownership of end-to-end pipelines rather than isolated, one-off scripts. • Early leadership instincts: mentoring juniors, improving team practices, or driving a project to completion without being told to. Watch-Outs • Buzzword stuffing without depth — lists every tool but can’t go deep on any. • Only coursework or certifications, with no applied projects. • Confusing data engineering with data analysis or pure ML work. Communication & Client-Facing Expectations This role works directly with US-based clients and stakeholders. Strong communication is a core requirement, not a nice-to-have: • Fluent, clear written and spoken English, with the ability to explain technical concepts to both technical and non-technical audiences. • Confident, articulate verbal communication suited to live client conversations, demos, and calls — neutral, easy-to-follow delivery that holds up across US time-zone meetings. • Active listening: ability to draw out what a client actually needs (often unstated), ask the right clarifying questions, and translate ambiguity into a clear plan. • Professional, composed presence on calls, with the maturity to manage expectations, disagree respectfully, and keep conversations productive without shutting them down. Good to have • knowledge of migrating e-commerce data • knowledge or working experience with DataBricks. Who Thrives Here We care about how people work, not only what they ship. The engineers and analysts who do best at Solvative tend to share a few traits: • Ownership without ego — they take end-to-end responsibility and raise the people around them rather than guarding knowledge. • Curiosity and depth — they can explain why they made a choice, not just what they used. • Directness balanced with empathy — honest, kind communication with teammates and clients alike. • A bias toward clarity — they turn messy, ambiguous requirements into something reliable and well-documented. Why Solvative? • Top-of-the-line Apple laptops for increased mobility and better productivity. • Medical insurance for all permanent employees. • The opportunity to work with an organization that believes in investing in its employees’growth. • An informal work environment that lets you have fun while being productive. • Lots of fun activities: we take over a nearby restaurant every last Friday of the month, tickets to all Marvel movies for the entire team, company picnics, and more!

Requirements

Must-haves

Job Title: Data Engineer
Location: Remote (India)
Development Center: Ahmedabad, Gujarat, India
Headquarters: Dallas, USA
Experience: 2+ years
You will collaborate with cross-functional teams to deliver reliable, high-quality data solutions while keeping performance,
governance, and operational excellence in view.
What You’ll Do
Design, build, and maintain robust batch and streaming data pipelines from ingestion through transformation to consumption.
Write clean, performant SQL and code (Python or Scala/Java) that others can read, test,and extend.
Model data thoughtfully — dimensional models, star/snowflake schemas, and the right warehouse/lake/lakehouse pattern for the problem.
Own pipelines end-to-end: monitoring, data quality, observability, lineage, and cost.
Read and analyze existing pipelines and code to debug issues and improve reliability and performance at scale.
Partner with analysts, data scientists, and business stakeholders to turn ambiguous requirements into dependable data products.
Core Technical Skills
SQL — SQL proficiency (non-negotiable): window functions, query optimization — not just basic SELECTs.
Programming — At least one strong programming language: Python or Scala/Java.
Pipelines — ETL/ELT tooling such as Airflow, dbt, Dagster, or Luigi.
Distributed processing — Distributed processing with Spark or Flink (Hadoop acceptable for legacy contexts).
Cloud — Cloud platforms (AWS, GCP, or Azure) and their data services — Redshift,BigQuery, Snowflake, or Databricks.
Formats — File formats (Parquet, Avro, ORC) and a clear understanding of when andwhy each matters.
Engineering Fundamentals (what separates good from average)
Version control (Git) and solid CI/CD practices.
Code quality: testing, modularity, and documentation.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

sqlpythonjavaawsgcpazureci/cddockerkubernetesgoml

Platform: breezy
Effort: medium

Benefits

Benefits detected in the listing.

Pto