Role:Data / Analytics Engineer – Contract

Experience: 5+ years

Mode: Remote

Time Zone: North America time zones

Duration: Long Term

About the Role

We are hiring a mid-to-senior Data / Analytics Engineer on a contract basis to work on the data platform behind our B2B data products.

You will build and operate pipelines that ingest large volumes of third-party data, transform and enrich it, and deliver curated datasets into the products that use them.

You will work across every layer of the stack, including orchestration (Airflow, Step Functions), transformation and processing (dbt, Spark, Athena and Iceberg SQL, Lambda), storage (Snowflake, S3, Glue), and the infrastructure that defines it all (Terraform).

This role also sits at the intersection of data engineering and AI engineering. You will integrate LLM and ML inference into production data workflows and build with AI coding agents as a standard part of how we work.

What You’ll Work On

- Build ingestion pipelines using Airflow DAGs that carry third-party data feeds end to end, from raw landing through normalization and enrichment to downstream publication.
- Build scheduled and event-driven pipelines on AWS using Step Functions state machines orchestrating Athena and Iceberg SQL, Glue and PySpark jobs, and Go Lambdas across both batch and streaming ingestion paths.
- Define pipelines as infrastructure, with state machines, Glue jobs, schedules, IAM roles, and alarms managed in Terraform.
- Model data using dbt on Snowflake and design/maintain Iceberg tables, including partitioning, MERGE, OPTIMIZE, and VACUUM.
- Tune pipelines for scale and cost, working with large datasets where query design, runtime, and compute spend impact every decision.
- Run models over large datasets through scheduled batch jobs, managing caching, batch size, cost,



output validation, and partial failures.
- Deliver curated datasets and audiences to downstream products and external platforms while working within rate limits, concurrency caps, and partial-failure tolerances.
- Own data quality and operations, including tests, freshness checks, alarms, anomaly investigation, failed-run triage, and root-cause fixes.
- Ship across the full stack, taking features from the warehouse through APIs to the UI, including unfamiliar parts of the codebase.

Required Skills

- 5+ years of experience in data engineering, analytics engineering, or a related field.
- Strong Python skills for production-quality, well-tested code, not just scripting.
- Advanced SQL experience at scale on cloud data warehouses; Snowflake is strongly preferred, while BigQuery, Databricks, or Redshift experience is transferable.
- Experience writing and tuning queries over very large tables while managing performance and cost.
- Strong dbt experience, including modeling patterns, incremental models, testing, project structure, and schema-change management.
- Production experience with Apache Airflow, including DAG authoring, scheduling, retries, backfills, and failure handling.
- Strong data modeling skills, including dimensional modeling, incremental and slowly changing patterns, and semi-structured data such as JSON, Parquet, and Avro.
- Experience with Spark/PySpark and lakehouse formats,



including Iceberg, Athena, and Glue.
- Hands-on AWS data services experience, including S3, Lambda, Step Functions, EventBridge, Kinesis, Firehose, SNS, SQS, and Secrets Manager.
- Solid Terraform experience. Pipelines are treated as infrastructure, including state machines, Glue jobs, schedules, IAM policies, and alarms.
- Ability to work across the full stack. Backend services use Go, REST APIs, Postgres, and serverless, while the frontend uses React and TypeScript. You do not need to be a specialist, but you should be comfortable reading and writing production code in unfamiliar languages.
- Production LLM integration experience with APIs such as Anthropic, OpenAI, AWS Bedrock, or equivalent, including structured outputs, tool calling, retries, rate limits, and token/cost management.
- Experience building LLM-powered product features, including streaming responses, tool-calling loops with step limits, and per-surface system prompts.
- Understanding of LLM security considerations, including tenant-scoped tool execution and protection against prompt injection.
- Experience with prompt engineering and systematic evaluation, including golden datasets, regression evaluations, and LLM-as-judge workflows.
- Fluency with agentic AI coding tools such as Claude Code or equivalent, including context design, task decomposition, reviewing output, and owning AI-assisted development.

Nice to Have
- Data science experience, including training, evaluating, and shipping in-house models using SageMaker.
- Streaming ingestion experience at scale with Kinesis, Firehose, or Kafka, including dynamic partitioning and schema evolution.
- Familiarity with data contracts and data observability practices.

📌 Data / Analytics Engineer (India)
🏢 Digivance Solutions
📍 India